{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "69c79f9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 结论：WOE + 逻辑回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "id": "acfe9e8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import toad\n",
    "from sklearn.ensemble import IsolationForest\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import roc_auc_score, roc_curve,auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "id": "ff2e6068",
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_data = pd.read_csv('data/Bcard.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "id": "8ccd85ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 95806 entries, 0 to 95805\n",
      "Data columns (total 13 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   obs_mth       95806 non-null  object \n",
      " 1   bad_ind       95806 non-null  float64\n",
      " 2   uid           95806 non-null  object \n",
      " 3   td_score      95806 non-null  float64\n",
      " 4   jxl_score     95806 non-null  float64\n",
      " 5   mj_score      95806 non-null  float64\n",
      " 6   rh_score      95806 non-null  float64\n",
      " 7   zzc_score     95806 non-null  float64\n",
      " 8   zcx_score     95806 non-null  float64\n",
      " 9   person_info   95806 non-null  float64\n",
      " 10  finance_info  95806 non-null  float64\n",
      " 11  credit_info   95806 non-null  float64\n",
      " 12  act_info      95806 non-null  float64\n",
      "dtypes: float64(11), object(2)\n",
      "memory usage: 9.5+ MB\n"
     ]
    }
   ],
   "source": [
    "data = raw_data.copy()\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa6effcf",
   "metadata": {},
   "source": [
    "# 字段说明：\n",
    "- bad_ind 为标签\n",
    "- 外部评分数据:td_score,jxl_score,mj_score,rh_score,zzc_score,zcx_score\n",
    "- 内部数据: person_info, finance_info, credit_info, act_info\n",
    "- obs_month: 申请日期所在月份的最后一天(数据经过处理,将日期都处理成当月最后一天"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "id": "0d01be2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>size</th>\n",
       "      <th>missing</th>\n",
       "      <th>unique</th>\n",
       "      <th>mean_or_top1</th>\n",
       "      <th>std_or_top2</th>\n",
       "      <th>min_or_top3</th>\n",
       "      <th>1%_or_top4</th>\n",
       "      <th>10%_or_top5</th>\n",
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       "      <th>99%_or_bottom2</th>\n",
       "      <th>max_or_bottom1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>obs_mth</th>\n",
       "      <td>object</td>\n",
       "      <td>95806</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>5</td>\n",
       "      <td>2018-07-31:35.52%</td>\n",
       "      <td>2018-06-30:21.47%</td>\n",
       "      <td>2018-11-30:16.67%</td>\n",
       "      <td>2018-10-31:15.16%</td>\n",
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       "      <td>2018-11-30:16.67%</td>\n",
       "      <td>2018-10-31:15.16%</td>\n",
       "      <td>2018-09-30:11.18%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bad_ind</th>\n",
       "      <td>float64</td>\n",
       "      <td>95806</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>2</td>\n",
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       "      <td>0.135702</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>uid</th>\n",
       "      <td>object</td>\n",
       "      <td>95806</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>95806</td>\n",
       "      <td>A10000005:0.00%</td>\n",
       "      <td>A7627440:0.00%</td>\n",
       "      <td>A7628676:0.00%</td>\n",
       "      <td>A762859:0.00%</td>\n",
       "      <td>A762848:0.00%</td>\n",
       "      <td>A265871:0.00%</td>\n",
       "      <td>A2658235:0.00%</td>\n",
       "      <td>A2657810:0.00%</td>\n",
       "      <td>A2657584:0.00%</td>\n",
       "      <td>Ab99_96436392001380983:0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>td_score</th>\n",
       "      <td>float64</td>\n",
       "      <td>95806</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>95806</td>\n",
       "      <td>0.499739</td>\n",
       "      <td>0.288349</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.009613</td>\n",
       "      <td>0.099706</td>\n",
       "      <td>0.500719</td>\n",
       "      <td>0.747984</td>\n",
       "      <td>0.900024</td>\n",
       "      <td>0.990041</td>\n",
       "      <td>0.999999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jxl_score</th>\n",
       "      <td>float64</td>\n",
       "      <td>95806</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>95806</td>\n",
       "      <td>0.499338</td>\n",
       "      <td>0.28885</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.009947</td>\n",
       "      <td>0.099103</td>\n",
       "      <td>0.499795</td>\n",
       "      <td>0.748646</td>\n",
       "      <td>0.899703</td>\n",
       "      <td>0.989348</td>\n",
       "      <td>0.999985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mj_score</th>\n",
       "      <td>float64</td>\n",
       "      <td>95806</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>95806</td>\n",
       "      <td>0.50164</td>\n",
       "      <td>0.288679</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.010508</td>\n",
       "      <td>0.100882</td>\n",
       "      <td>0.503048</td>\n",
       "      <td>0.752032</td>\n",
       "      <td>0.899308</td>\n",
       "      <td>0.990047</td>\n",
       "      <td>0.999993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rh_score</th>\n",
       "      <td>float64</td>\n",
       "      <td>95806</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>95806</td>\n",
       "      <td>0.498407</td>\n",
       "      <td>0.287797</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.009916</td>\n",
       "      <td>0.099948</td>\n",
       "      <td>0.497466</td>\n",
       "      <td>0.747188</td>\n",
       "      <td>0.899286</td>\n",
       "      <td>0.989473</td>\n",
       "      <td>0.999986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zzc_score</th>\n",
       "      <td>float64</td>\n",
       "      <td>95806</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>95806</td>\n",
       "      <td>0.500627</td>\n",
       "      <td>0.289067</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.010186</td>\n",
       "      <td>0.099011</td>\n",
       "      <td>0.501688</td>\n",
       "      <td>0.750986</td>\n",
       "      <td>0.899924</td>\n",
       "      <td>0.990043</td>\n",
       "      <td>0.999998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zcx_score</th>\n",
       "      <td>float64</td>\n",
       "      <td>95806</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>95806</td>\n",
       "      <td>0.499672</td>\n",
       "      <td>0.289137</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>0.010325</td>\n",
       "      <td>0.099743</td>\n",
       "      <td>0.49913</td>\n",
       "      <td>0.750683</td>\n",
       "      <td>0.901942</td>\n",
       "      <td>0.989712</td>\n",
       "      <td>0.999987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>person_info</th>\n",
       "      <td>float64</td>\n",
       "      <td>95806</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>7</td>\n",
       "      <td>-0.078229</td>\n",
       "      <td>0.156859</td>\n",
       "      <td>-0.322581</td>\n",
       "      <td>-0.322581</td>\n",
       "      <td>-0.322581</td>\n",
       "      <td>-0.053718</td>\n",
       "      <td>0.078853</td>\n",
       "      <td>0.078853</td>\n",
       "      <td>0.078853</td>\n",
       "      <td>0.078853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>finance_info</th>\n",
       "      <td>float64</td>\n",
       "      <td>95806</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>35</td>\n",
       "      <td>0.036763</td>\n",
       "      <td>0.039687</td>\n",
       "      <td>0.02381</td>\n",
       "      <td>0.02381</td>\n",
       "      <td>0.02381</td>\n",
       "      <td>0.02381</td>\n",
       "      <td>0.02381</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>1.02381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>credit_info</th>\n",
       "      <td>float64</td>\n",
       "      <td>95806</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>100</td>\n",
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       "      <td>0.06</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>act_info</th>\n",
       "      <td>float64</td>\n",
       "      <td>95806</td>\n",
       "      <td>0.00%</td>\n",
       "      <td>74</td>\n",
       "      <td>0.236197</td>\n",
       "      <td>0.157132</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>0.076923</td>\n",
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       "      <td>0.346154</td>\n",
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      ],
      "text/plain": [
       "                 type   size missing  unique       mean_or_top1  \\\n",
       "obs_mth        object  95806   0.00%       5  2018-07-31:35.52%   \n",
       "bad_ind       float64  95806   0.00%       2           0.018767   \n",
       "uid            object  95806   0.00%   95806    A10000005:0.00%   \n",
       "td_score      float64  95806   0.00%   95806           0.499739   \n",
       "jxl_score     float64  95806   0.00%   95806           0.499338   \n",
       "mj_score      float64  95806   0.00%   95806            0.50164   \n",
       "rh_score      float64  95806   0.00%   95806           0.498407   \n",
       "zzc_score     float64  95806   0.00%   95806           0.500627   \n",
       "zcx_score     float64  95806   0.00%   95806           0.499672   \n",
       "person_info   float64  95806   0.00%       7          -0.078229   \n",
       "finance_info  float64  95806   0.00%      35           0.036763   \n",
       "credit_info   float64  95806   0.00%     100           0.063626   \n",
       "act_info      float64  95806   0.00%      74           0.236197   \n",
       "\n",
       "                    std_or_top2        min_or_top3         1%_or_top4  \\\n",
       "obs_mth       2018-06-30:21.47%  2018-11-30:16.67%  2018-10-31:15.16%   \n",
       "bad_ind                0.135702                0.0                0.0   \n",
       "uid              A7627440:0.00%     A7628676:0.00%      A762859:0.00%   \n",
       "td_score               0.288349           0.000005           0.009613   \n",
       "jxl_score               0.28885           0.000013           0.009947   \n",
       "mj_score               0.288679           0.000007           0.010508   \n",
       "rh_score               0.287797           0.000005           0.009916   \n",
       "zzc_score              0.289067           0.000012           0.010186   \n",
       "zcx_score              0.289137            0.00001           0.010325   \n",
       "person_info            0.156859          -0.322581          -0.322581   \n",
       "finance_info           0.039687            0.02381            0.02381   \n",
       "credit_info            0.143098                0.0                0.0   \n",
       "act_info               0.157132           0.076923           0.076923   \n",
       "\n",
       "                    10%_or_top5     50%_or_bottom5     75%_or_bottom4  \\\n",
       "obs_mth       2018-09-30:11.18%  2018-07-31:35.52%  2018-06-30:21.47%   \n",
       "bad_ind                     0.0                0.0                0.0   \n",
       "uid               A762848:0.00%      A265871:0.00%     A2658235:0.00%   \n",
       "td_score               0.099706           0.500719           0.747984   \n",
       "jxl_score              0.099103           0.499795           0.748646   \n",
       "mj_score               0.100882           0.503048           0.752032   \n",
       "rh_score               0.099948           0.497466           0.747188   \n",
       "zzc_score              0.099011           0.501688           0.750986   \n",
       "zcx_score              0.099743            0.49913           0.750683   \n",
       "person_info           -0.322581          -0.053718           0.078853   \n",
       "finance_info            0.02381            0.02381            0.02381   \n",
       "credit_info                 0.0                0.0               0.06   \n",
       "act_info               0.076923           0.205128           0.346154   \n",
       "\n",
       "                 90%_or_bottom3     99%_or_bottom2  \\\n",
       "obs_mth       2018-11-30:16.67%  2018-10-31:15.16%   \n",
       "bad_ind                     0.0                1.0   \n",
       "uid              A2657810:0.00%     A2657584:0.00%   \n",
       "td_score               0.900024           0.990041   \n",
       "jxl_score              0.899703           0.989348   \n",
       "mj_score               0.899308           0.990047   \n",
       "rh_score               0.899286           0.989473   \n",
       "zzc_score              0.899924           0.990043   \n",
       "zcx_score              0.901942           0.989712   \n",
       "person_info            0.078853           0.078853   \n",
       "finance_info           0.071429           0.214286   \n",
       "credit_info                0.18                0.8   \n",
       "act_info               0.487179           0.615385   \n",
       "\n",
       "                            max_or_bottom1  \n",
       "obs_mth                  2018-09-30:11.18%  \n",
       "bad_ind                                1.0  \n",
       "uid           Ab99_96436392001380983:0.00%  \n",
       "td_score                          0.999999  \n",
       "jxl_score                         0.999985  \n",
       "mj_score                          0.999993  \n",
       "rh_score                          0.999986  \n",
       "zzc_score                         0.999998  \n",
       "zcx_score                         0.999987  \n",
       "person_info                       0.078853  \n",
       "finance_info                       1.02381  \n",
       "credit_info                            1.0  \n",
       "act_info                          1.089744  "
      ]
     },
     "execution_count": 333,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "toad.detector.detect(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2017712",
   "metadata": {},
   "source": [
    "- 该数据已经经过处理了，没有缺失值，且没有异常值，我们直接进行下一步的处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "id": "8b2ab338",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-07-31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A1000002</td>\n",
       "      <td>0.825269</td>\n",
       "      <td>0.398688</td>\n",
       "      <td>0.139396</td>\n",
       "      <td>0.843725</td>\n",
       "      <td>0.605194</td>\n",
       "      <td>0.406122</td>\n",
       "      <td>-0.128677</td>\n",
       "      <td>0.023810</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.423077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-09-30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A1000011</td>\n",
       "      <td>0.315406</td>\n",
       "      <td>0.629745</td>\n",
       "      <td>0.535854</td>\n",
       "      <td>0.197392</td>\n",
       "      <td>0.614416</td>\n",
       "      <td>0.320731</td>\n",
       "      <td>0.062660</td>\n",
       "      <td>0.023810</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.448718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-07-31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A10000481</td>\n",
       "      <td>0.002386</td>\n",
       "      <td>0.609360</td>\n",
       "      <td>0.366081</td>\n",
       "      <td>0.342243</td>\n",
       "      <td>0.870006</td>\n",
       "      <td>0.288692</td>\n",
       "      <td>0.078853</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.179487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-07-31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A1000069</td>\n",
       "      <td>0.406310</td>\n",
       "      <td>0.405352</td>\n",
       "      <td>0.783015</td>\n",
       "      <td>0.563953</td>\n",
       "      <td>0.715454</td>\n",
       "      <td>0.512554</td>\n",
       "      <td>-0.261014</td>\n",
       "      <td>0.023810</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.423077</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      obs_mth  bad_ind        uid  td_score  jxl_score  mj_score  rh_score  \\\n",
       "0  2018-10-31      0.0  A10000005  0.675349   0.144072  0.186899  0.483640   \n",
       "1  2018-07-31      0.0   A1000002  0.825269   0.398688  0.139396  0.843725   \n",
       "2  2018-09-30      0.0   A1000011  0.315406   0.629745  0.535854  0.197392   \n",
       "3  2018-07-31      0.0  A10000481  0.002386   0.609360  0.366081  0.342243   \n",
       "4  2018-07-31      0.0   A1000069  0.406310   0.405352  0.783015  0.563953   \n",
       "\n",
       "   zzc_score  zcx_score  person_info  finance_info  credit_info  act_info  \n",
       "0   0.928328   0.369644    -0.322581      0.023810         0.00  0.217949  \n",
       "1   0.605194   0.406122    -0.128677      0.023810         0.00  0.423077  \n",
       "2   0.614416   0.320731     0.062660      0.023810         0.10  0.448718  \n",
       "3   0.870006   0.288692     0.078853      0.071429         0.05  0.179487  \n",
       "4   0.715454   0.512554    -0.261014      0.023810         0.00  0.423077  "
      ]
     },
     "execution_count": 334,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "id": "7219ce3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 1.])"
      ]
     },
     "execution_count": 335,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['bad_ind'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e240fdd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "id": "c3a2e729",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. 取出可以作为特征的列\n",
    "## 其中去掉了标签bad_ind，以及uid、obs_mth这些看起来就比较无关的列\n",
    "feature_list = ['td_score', 'jxl_score', 'mj_score', 'rh_score', \n",
    "                'zzc_score', 'zcx_score', 'person_info', 'finance_info', \n",
    "                'credit_info', 'act_info']\n",
    "\n",
    "exclude_list = ['bad_ind', 'uid', 'obs_mth']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "id": "0d58c95b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2018-10-31', '2018-07-31', '2018-09-30', '2018-06-30',\n",
       "       '2018-11-30'], dtype=object)"
      ]
     },
     "execution_count": 337,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# 2. 特征工程\n",
    "# 2.1 划分训练集、测试集、跨时间样本\n",
    "data['obs_mth'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "id": "7432b14c",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = data[data['obs_mth'] != '2018-11-30']\n",
    "val_data = data[data['obs_mth'] == '2018-11-30']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "id": "fa889d4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data, test_data = train_test_split(train_data, test_size=0.2, random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "id": "a99caf0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\ttraining's auc: 0.781764\tvalid_1's auc: 0.776026\n",
      "[2]\ttraining's auc: 0.805452\tvalid_1's auc: 0.794586\n",
      "[3]\ttraining's auc: 0.814389\tvalid_1's auc: 0.801602\n",
      "[4]\ttraining's auc: 0.816455\tvalid_1's auc: 0.806278\n",
      "[5]\ttraining's auc: 0.818698\tvalid_1's auc: 0.807208\n",
      "[6]\ttraining's auc: 0.822558\tvalid_1's auc: 0.808296\n",
      "[7]\ttraining's auc: 0.823295\tvalid_1's auc: 0.809409\n",
      "[8]\ttraining's auc: 0.826965\tvalid_1's auc: 0.809386\n",
      "[9]\ttraining's auc: 0.826527\tvalid_1's auc: 0.809453\n",
      "[10]\ttraining's auc: 0.826859\tvalid_1's auc: 0.8092\n",
      "[11]\ttraining's auc: 0.826766\tvalid_1's auc: 0.808738\n",
      "[12]\ttraining's auc: 0.827961\tvalid_1's auc: 0.809416\n",
      "[13]\ttraining's auc: 0.827948\tvalid_1's auc: 0.809135\n",
      "[14]\ttraining's auc: 0.828804\tvalid_1's auc: 0.809695\n",
      "[15]\ttraining's auc: 0.829628\tvalid_1's auc: 0.809478\n",
      "[16]\ttraining's auc: 0.830596\tvalid_1's auc: 0.809352\n",
      "[17]\ttraining's auc: 0.831124\tvalid_1's auc: 0.809318\n",
      "[18]\ttraining's auc: 0.831663\tvalid_1's auc: 0.809233\n",
      "[19]\ttraining's auc: 0.833537\tvalid_1's auc: 0.810258\n",
      "[20]\ttraining's auc: 0.834424\tvalid_1's auc: 0.809916\n",
      "[21]\ttraining's auc: 0.835452\tvalid_1's auc: 0.8095\n",
      "[22]\ttraining's auc: 0.836214\tvalid_1's auc: 0.809858\n",
      "[23]\ttraining's auc: 0.8366\tvalid_1's auc: 0.809942\n",
      "[24]\ttraining's auc: 0.83757\tvalid_1's auc: 0.810427\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>person_info</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>finance_info</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>credit_info</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>act_info</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mj_score</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>zzc_score</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>rh_score</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>zcx_score</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>td_score</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>jxl_score</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           name  importance\n",
       "6   person_info          56\n",
       "7  finance_info          50\n",
       "8   credit_info          49\n",
       "9      act_info          49\n",
       "2      mj_score          35\n",
       "4     zzc_score          30\n",
       "3      rh_score          27\n",
       "5     zcx_score          26\n",
       "0      td_score          19\n",
       "1     jxl_score          19"
      ]
     },
     "execution_count": 340,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# 2.1 首先初步的筛选出一些比较重要的特征（特征初筛选）\n",
    "def lgb_test(train_x,train_y,test_x,test_y): \n",
    "    import lightgbm as lgb \n",
    "    clf = lgb.LGBMClassifier(boosting_type = 'gbdt', \n",
    "                             objective = 'binary', \n",
    "                             metric = 'auc', \n",
    "                             learning_rate = 0.1,\n",
    "                             n_estimators = 24, \n",
    "                             max_depth = 4, \n",
    "                             num_leaves = 25, \n",
    "                             max_bin = 40, \n",
    "                             min_data_in_leaf = 5, \n",
    "                             bagging_fraction = 0.6, \n",
    "                             bagging_freq = 0, \n",
    "                             feature_fraction = 0.8,)\n",
    "    clf.fit(train_x, train_y, \n",
    "            eval_set=[(train_x, train_y), \n",
    "                      (test_x,test_y)], \n",
    "            eval_metric = 'auc')\n",
    "    \n",
    "    return clf,clf.best_score_['valid_1']['auc']\n",
    "\n",
    "lgb_model, lgb_auc = lgb_test(train_data[feature_list], train_data['bad_ind'], test_data[feature_list], test_data['bad_ind'])\n",
    "feature_importance = pd.DataFrame({'name':lgb_model.booster_.feature_name(), \n",
    "                                   'importance':lgb_model.feature_importances_}).sort_values(by= ['importance'],ascending=False)\n",
    "feature_importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "id": "d6734cf8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 这里我经过修改random_state，并使用lightgdm进行测试，最终得出下面这4个比较重要的特征\n",
    "feature_list = ['person_info', 'finance_info', 'credit_info', 'act_info']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "id": "839b30a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "IForest(behaviour='new', bootstrap=False, contamination=0.1, max_features=1.0,\n",
       "    max_samples='auto', n_estimators=500, n_jobs=-1, random_state=None,\n",
       "    verbose=0)"
      ]
     },
     "execution_count": 342,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 异常点检测\n",
    "from pyod.models.iforest import IForest\n",
    "clf = IForest(behaviour='new',n_estimators=500, n_jobs=-1)\n",
    "clf.fit(train_data[feature_list])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "id": "77a6a4a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\python\\software\\base\\lib\\site-packages\\sklearn\\base.py:444: UserWarning: X has feature names, but IsolationForest was fitted without feature names\n",
      "  f\"X has feature names, but {self.__class__.__name__} was fitted without\"\n"
     ]
    }
   ],
   "source": [
    "out_pred = clf.predict_proba(train_data[feature_list], method ='linear')[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "id": "9b435604",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['out_pred'] = out_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "id": "133fc205",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = train_data[train_data['out_pred'] < 0.7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "id": "71f75eea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(62409, 14)"
      ]
     },
     "execution_count": 346,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "88574251",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "id": "da07ff23",
   "metadata": {},
   "outputs": [],
   "source": [
    "woe_train_x = train_data[feature_list]\n",
    "woe_train_y = train_data['bad_ind']\n",
    "woe_train_data = pd.concat([woe_train_x, woe_train_y], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "id": "9ffcdedf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<toad.transform.Combiner at 0x2a5f3c08978>"
      ]
     },
     "execution_count": 348,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 特征变换，WOE处理\n",
    "\n",
    "combiner = toad.transform.Combiner()\n",
    "combiner.fit(woe_train_x, woe_train_y, method='chi', min_samples=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "id": "0c1201a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'person_info': [-0.2610139784946237, -0.1286774193548387, -0.0537175627240143, 0.013863440860215, 0.0626602150537634, 0.078853046594982], 'finance_info': [0.0476190476190476, 0.0714285714285714], 'credit_info': [0.02, 0.04, 0.09, 0.14], 'act_info': [0.1153846153846154, 0.2051282051282051, 0.358974358974359, 0.4615384615384616, 0.5256410256410257]}\n"
     ]
    }
   ],
   "source": [
    "# 导出箱的节点 \n",
    "bins = combiner.export() \n",
    "print(bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "id": "43b4eb23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a5f74310f0>"
      ]
     },
     "execution_count": 350,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pBgpRIiIiIiI/QyR3J3uyn8KJREkeeIbb5Ug10MISIiIiIlJrpTdOIeD3uXb+0M6dbPjbZDzhEB3+9CeS2rd3rZbDCYYj7MnLd7uMOkchSkRERERqrYDfx6SZK105d+KBvfT5z99JCBYyf9il7N1wADa4U8uR3Daou9sl1EmaziciIiIiUkGJ+fs4bfoUEooKmH/Ob9jbrKXbJUk1UogSEREREamAQMEBek+fQmL+ARaecwl7M1q5XZJUM4UoEREREZFySijIp/f0KSQd2MfCsy9id/M2bpckLlCIEhEREREph4TCAnp/MIWUfbtZdNaF5LVs53ZJ4hKFKBERERGRMviLCjn1w3+SuncXi35xAbtadXC7JHGRQpSIiIiIyFH4g0Wc+uG/SMvbyeIzzye3TSe3SxKXKUSJiIiIiByBL1TEKR/9i4a521h85nnsbNfZ7ZKkBlCIEhERERE5DF8oyCkfvUn6jh/45oyR7Gh/vNslSQ2hECUiIiIiUoo3HOLkT/6Pxts3s3Twr9jW0bhdktQgClEiIiIiIiV4w2FO/uQtmvzwHUsHjmDrcd3dLklqGIUoEREREZE4TyTMiZ+9TbMtG1g2YDg/dOnpdklSAylEiYiIiIgAnkiEEz+fRvNN61jW7xw2d+3ldklSQylEiYiIiEi954lGOGHGO7T4bg0rTj+LTd1OdLskqcEUokRERESkXvNEo/Sa8R9abviWlX3O5LseJ7tdktRwClEiIiIiUn9Fo2TOep9W61eyqvcQNvbs7XZFUgsoRImIiIhI/eQ4ZM75gDZrl/PtyQPZkNXH7YqkllCIEhEREZH6x3Ho+eWHtF2dw5oT+7PuxH5uVyS1iEKUiIiIiNQvjkP3rz6hnf2Gtb1OZ81J/d2uSGoZhSgRERERqT8ch25ff0aHlYtYn9mb1acMAo/H7aqkllGIEhEREZH6wXHoumAGHZcvYEOPU7C9z1CAkmOiECUiIiIi9UKXRbM4Lmce33U7iVV9hipAyTFTiBIRERGROq/zkjl0+WYu33ftxYq+ZylAyc+iECUiIiIidVqnb77i+EWz2dwlk+X9hylAyc+mECUiIiIidVbHZV9jFs5gy3E9yBkwXAFKKoVClIiIiIjUSe1XLKTb15/zQ0dDzqAR4NVPX6kc+iaJiIiISJ3TbtVienz1CdvaH8/SIb/CUYCSSqRvk4iIiIjUKW2+/YaeX37E9nadWXLGeThen9slSR2jECUiIiIidUbr1cvInP0BO9p0YvGZv8bxKUBJ5VOIEhEREZE6odXaFWTNfp/cVh1YPPR8HJ/f7ZKkjlKIEhEREZFar8X6VWTNfI+8Fm1ZdNaFRP0JbpckdZhClIiIiIjUas03fssJX7zLnozWLDzrIgUoqXIKUSIiIiJSa+1bsoQTP5/G3mYtWHD2xUQSAm6XJPWAJoqKiIiISK0UtCvJfeV59jVpzoKzLyESSHS7JDkGxphrgVuBfcDl1toNJdqaAlOAVsAUa+0EY4wfeBQYSGxQ6Dpr7QJjjAG+An7cf5K19rWqqFkhSkRERERqneBqy95X/kZi69YsGPhrwolJbpckx8AYkwHcAfQCBgOPAReU6DIe+DfwLDDHGPMOcABYYK0da4wZATwB9AfSgX9ba39f1XUrRLmsjOQ9EvgT0BR43Vr7l/j2uUAKEAXWWmsvqu66RURERNwSWruavS9n42uWQftx4wgt3uR2SXLszgYWWmvzjTEfAq8YYzzWWifePgIYaa2NGmPeBEZYax8E1sbbZwPt4+/Tgd3VUbRClIvKkby7Ab8AHMAaY6Zaa78F0oBe1tpoNZcsIiIi4qrQ+nXseek5fI2bkj76JvxpaW6XJEcwffr0lmPHju1YavNua23JoNMKsADxoJQHNAFy4+3N+SkwbSY24lRSH2BR/H068AtjzGJgIzDGWru1Mq6lNC0s4a7i5A18CPQ3xnh+bLTWPmytLbDWFhL7crQr0aYAJSIiIvVKaON69r74LL70RqSPvglvAwWomuyRRx6ZCqwv9Rp7mK4lM0kasQGEkjyHa4vfG3UvsfujAD4ArgT6At8DE37WBRyFQpS7DkrewI/J+yDxL0gvICe+qZkx5mNjzGJjzK8Od2BjTCNjTMeSry+++KJl1VyGiIiISNUKff8de194Fk+DBjQcfTPehululyRlGDdu3MVAp1Kvx0t12wIYAGNMGtCY2G/iH20DusTfm3j/H70ATLfWzgKw1u631i6OD0C8RGxWV5XQdD73lZW8AcYAn1lrt8c//wpYBpwC/McY09JaW1Bqn7HAPSU3ZGdnM2TIEBo3Tq2cykVERESqQeHGjWx88Rn8DVLpcOedJDRt6nZJtUpGhjsjdsOHD986fPjwDWV0+wi43xiTAgwBpgOXGGNaW2snAe8BZxhjVhK7/eUaAGPMQ0DAWvvnHw9kjLkk3r8QGAksqNwr+olClLu2EJvHeaTkjTHmbOAq4Iwft1lr58ffzjbGbADaAGtKHftx4JWSG0aPHn0qMDUv7wDhsGYDioiISM0X/mELe557Ek8gQIPrbmR3NAA79hW3uxUQapMdJf5+VQe/31vuP7S31u40xjwAzCO+0BqxNQI6xrtMILbE+RjgH9baHGPMcGKLry2M3/8EcAsQAj4GWhAbcLi6Mq7ncDyOc7iBD6kOxphmxNay7wUMJfYP+l9Aa2vtJGPMqcS+NGdaa7+L73MyELbWLjXGZAL/ATpba8PlOOUAYJZClIiIiNQG4W0/sOfZJ/H4fKTfcCu+ZhmH9MnISGPSzJUuVFc73Daou5shaiCx1fPqHI1Euagcyft9IB94yxjjAz4htg7+E8aYzkAicHU5A5SIiIhIrRHZsZ092ZPB66Xh6JsPG6BE3KIQ5TJr7UvEbnz70WMl2pofYbcLq7QoERERERdFdu5gT/ZTEI2SfsMt+Ju3cLskkYNodT4RERERqTEiu3LZk/0UTihE+uib8bdo5XZJIodQiBIRERGRGiGyO489zz2JU1RE+vU34W/V2u2SRA5LIUpEREREXBfZszsWoAoKaHjdjfjbtHW7JJEjUogSEREREVdF9+5lb/ZTOPv30/C6P5DQrr3bJYkclUKUiIiIiLgmun8fe7KfIrJnNw1HjSGhfUe3SxIpk0KUiIiIiLgiemA/e7InE8nLJf2aMSR06ux2SSLlohAlIiIiItUumn+APc8/TWTnDhr+fjQJnY93uySRclOIEhEREZFqFS3IZ+/fniGybSsNr7qWwPHG7ZJEKkQhSkRERESqTbSwgL0vPEv4h800vHIUgW493C5JpMIUokRERESkWjhFRex96TnCm74j7YrfE+iR6XZJIsdEIUpEREREqpwTDLL35WzCG9aTdvnVJGae4HZJIsdMIUpEREREqpQTCrL3lecJrVtDg8uuJPGEk9wuSeRnUYgSERERkSrjhEPsffUFQmu+pcElvyXppFPdLknkZ1OIEhEREZEq4YTD7H3tJUJ2JQ0uuoykU/u4XZJIpVCIEhEREZFK50Qi7PvflwmtXEbqBb8h6bS+bpckUmkUokRERESkUjmRCPv+/irBZUtJPe9CkvsOcLskkUqlECUiIiIilcaJRtn/zzcILl1Myi9/TfKAIW6XJFLpFKJEREREpFI40Sj7//W/FC1eQMrwX5EyeKjbJYlUCYUoEREREfnZnGiU/f83haKFX5Ny9rmknHm22yWJVBmFKBERERH5WRzH4cC/p1L09VySh55D8i+GuV2SSJVSiBIRERGRY+Y4DgfeeYvCubNJHvILUs4ZgcfjcbsskSqlECUiIiIix8RxHPL/M43C2V+QNHAIKeeOVICSekEhSkREREQqzHEc8j94l4IZn5LUbyCpv7pAAUrqDYUoEREREamw/I+nU/DZxyT16U/qeRcpQEm9ohAlIiIiIhWS/+mHFHw8ncTep5N6wSV4vPpJKfWLvvEiIiIiUm75X3xC/gfvkXhybxpcdJkClNRL+taLiIiISLkUzPqc/P9MI3DCyTS45LcKUFJv6ZsvIiIiImUqmDOTA++8RSDrRNIuuxKPz+d2SSKuUYgSERERkaMqnDeHA/+eSqBHFmmXX6UAJfWeQpSIiIiIHFHh/K/Y/3//JKFbD9J+93s8fr/bJYm4TiFKRERERA6rcOF89k/9OwldDA2vvBaPP8HtkkRqBIUoERERETlE0TeL2P/P10k4rgsNr74OT4IClMiPNB4rIiIiUstFo1EmTnyINWtWk5CQwB13jKdt23bF7e+88zbTpr2Fz+fjqqtG0b//QLZu3cqDD95PJBIG4Pbb76J9+44AFOUsYd/fX8Xf8TgaXjMaTyDgxmWJ1FgaiRIRERGp5WbN+oJgMEh29suMGXMzkydPKm7Lzd3Jm29O4dlnX+SxxyaTnT2ZYDDICy88y4UXXsLkyc/zu9/9nueeexqAohU57HvjZfztOtDwmjF4AoluXZZIjaWRKBEREZFabunSJfTp0xeAzMwsVq1aWdy2cuVysrJOIBAIEAgEaNOmHWvXruamm26jQYMGAEQiEQKBAMFVy9n32kv427Sl4agb8CYluXI9IjWdRqJEREREarkDBw6Qmtqg+LPX6yUcDh+2LSUlhf3799OoUSP8fj/ffbeBp59+nCvOGMreV1/A17IlDa+9EW9ycrVfh0htoZEoERERkVouNTWV/Pz84s+O4+CPL0Veui0/P5+0tDQAFi1awMSJD3HH1dfS6IP38GU0J/26m/CmpFTvBYjUMhqJEhEREanlsrJO4Kuv5gCwbFkOxx3Xpbite/eeLF26mKKiIvbv38/Gjevp1KkzixYt4IknHuWhW/9Iq88/xte0KenX34Q3NdWtyxCpNTQSJSIiIlLLDRp0BvPnz2PMmGtwHIe77rqHKVPeoG3bdgwYMJiLLrqUG2+8jmg0yvXX/4HExESeeGIiwfx8/nLf/wO/n06n9eVPDdLcvhSph4wx1wK3AvuAy621G0q0NQWmAK2AKdbaCcYYP/AoMJDYoNB11toFxhgvMBkYBCwHrrLWFlZFzQpRIiIiIrWc1+tl3Li7DtrWoUPH4vcjR57PyJHnH9T+woSH2Pv8ZDwN0kgfcyu+9PTqKFXkIMaYDOAOoBcwGHgMuKBEl/HAv4FngTnGmHeAA8ACa+1YY8wI4AmgPzASyLDWZhpj/gpcDzxZFXVrOp/LjDHXGmNyjDFfGmM6lmobaYyZY4xZZYy5u8T2EcaYpcaYRcaYU6q9aBEREanVwpu+Z+/fnsaTkkr66JsVoMRNZwMLrbX5wIdAf2OMp0T7COAza20UeBMYYa1da619I94+G2hfsm/8/b/in6uERqJcVI7k3Q34BeAA1hgzFVhPbJiyD5ABvAYoSImIiEi5hLdsZs/fJuNJTIoFqEaN3S5J6qjp06e3HDt2bMdSm3dba3eX+NwKsADW2qgxJg9oAuTG25sDa+PvNxMbcSqpD7Co9LHifVv/3Gs4EoUodxUnb2PMh8ArxhiPtdYBsNY+/GNHY8wioB2x4LTdWrsd2G6MCRhjWllrfyh5YGNMI6BRyW3Z2dkthwwZUrVXJCIiIjVWeOsP7Hl+Mp6EAOljbsHXpKnbJUkd9sgjj0w9zOb7gHtLbSs5Oy6N2ABCSZ7DtcXvjboX+NNhjnW441QahSh3lZW8geIvSC8gh9iNcrZE848p+6AQBYwF7im5ITs7myFDhtC4sVbdERERqW+Ktmxh498m40vw0+GOOwi0bOl2SVJNMjLcWTBk3LhxF48dO3ZBqc27S33eQmw0CWNMGtAYyCvRvg3oQmyhCBPv/6MXgOnW2lkljmWATw7Tt1IpRLmvrOQNMIbYXNDtxpjy7vM48ErJDaNHjz4VmJqXd4BwOPpzahYREZFawgmHKcpZQv57b+NEHdJH38geXyrs2Od2aZXCrYBQm+yo5n/Wfr+Xxo1TGT58+Nbhw4dvKKP7R8D9xpgUYAgwHbjEGNPaWjsJeA84wxizktjtL9cAGGMeAgLW2j+XONZ7wJXA08AZwH8q8bIOohDlrrKSN8aYs4GriH0RftzHlOzCYVJ2fK5p6aTftlKqFhERkRovuncPBXNnU/jVHJz9+/BlNKfhlaPwN9cIlNQc1tqdxpjrojV7AAAgAElEQVQHgHnElzgntkZAx3iXCcSWOB8D/MNam2OMGU5sCt9CY8zieL9bgHeBs40xy4iNXB28ZGUl8jhOlU0VlDIYY5oBXxGbqjcUuJrYSiKtrbWTjDGnEvvSnGmt/S6+jx/4FuhL7Ea7V6y15V1YYgAwSyNRIiIidZPjOIQ3rqdgzgyCS5dANEpCtx4k9x9EQtfueLx1b2HmjIw0Js1c6XYZNdZtg7q7NhJF7DlOs6v15NVEI1EuKkfyfh/IB94yxviAT6y144wxNwIfA2Hg2movXERERGoUJxSiaMlCCubMJLL5ezxJSST1H0Ryv0H4mmW4XZ5InaMQ5TJr7UvASyU2PVairfkR9plObL6oiIiI1GORvF0Uzp1N4bwvcfIP4GvRktTzLyHplNPwJCa6XZ5InaUQJSIiIlKLOI5DaN0aCmfPILh8KQCBHlkkDRhEQueueDyeMo4gIj+XQpSIiIhILeAEiyhcOJ/CL2cS2foDnpQUkgcPJanvAD3vSaSaKUSJiIiI1GCR3J0UfDmTovlf4RQU4GvdlgYXX07iSafgSQi4XZ5IvaQQJSIiIlLDONEoodWWgjkzCK1aAR4PgawTSO4/GH/H4zRlT8RlClEiIiIiNUS0sICiBV/Hpuzt2I6nQRrJQ88h6fT++NIbuV2eiMQpRImIiIi4LLx9K4VzZlK08GucoiL87TrQ4NIrSTzhRDz+BLfLE5FSFKJEREREXOBEowRXLadw9kxCq1eBz0/iCSeR1H8QCe07ul2eiByFQpSIiIhINYrm51M4fy6FX84iuisXb8N0Us4ZQVKffnjTGrpdnoiUg0KUiIiISDUI/7CFgjkzKFo0H0Ih/J06k3rueQQye+Hx+dwuT0QqQCFKREREpIo4kQjB5TkUzJlBeN0a8CeQePKpJPcbhL9NW7fLE5FjpBAlIiIiUsmi+/dROO9LCufOJrpnN97GTUgZcR5Jp/XFm5LqdnkiEmeMOQlob62dFv+cZK0tLGs/hSgRERGRShL6/jsK58yg6JtFEA6T0KUrqb++mECPTDxer9vliUgJxpgHgFbA6cA0Y0xb4BlgZFn7KkSJiIiI/AxOOExRzhIKZ88g/N0GCARI6t2XpP4D8bdo5XZ5InJkv7bW9jDGLAKw1m6KB6kyKUSJiIiIHIPo3j0UfDWHwq/m4Ozbi7dZBqkjLyTx1D54k5PdLk9EynbAGNMccACMMX2BYHl2VIgSERERKSfHcQhvXE/BnBkEly6BaJSEbj1I7j+YhK7dNGVPpHYZA7wLHGeMmQW0BH5Tnh0VokRERETK4IRCFC1ZSMGcmUQ2f48nKYmk/oNI7jcIX7MMt8sTkWNgrV1ojOkHdAW8gAUi5dlXIUpERETkCCJ5uyicO5vCeV/i5B/A16IlqedfQtIpp+FJTHS7PBH5GYwxH1lrzwZWltg2Axhc1r4KUZXEGNMQuBU4BSgAPgNestaWK82KiIhIzeA4DqF1ayicPYPg8qUABHpkkTRgEAmdu+LxeFyuUER+DmPMacRW5OtmjLm5RFNbYqv1lUkhqvJMIZZinyE2DHg10Bu43sWa6qVoNMrEiQ+xZs1qEhISuOOO8bRt2+6gPnl5edxwwzW8+uoUEhMTiUQiPPXUJKxdQTAY4pprrqd//4EuXYGIiLjBCRZRuHA+hV/OJLL1BzwpKSQPGUpS34H4GjdxuzwRqTwFQIhYFmpcYvu3wBnlOYBCVOVpaa09t8TnT40xOa5VU4/NmvUFwWCQ7OyXWbYsh8mTJ/HQQ48Vt8+bN5fnnnuKXbt2FW/78MP3CYfDPPvsS+zYsZ3PP//EjdJFRMQFkdydFHw5k6L5X+EUFOBr3ZYGF19O4kmn4EkIuF2eiFQya20OkGOMWWGtnXEsx1CIqjxfGmN6WWuXAhhjWgJLXK6pXlq6dAl9+vQFIDMzi1WrVh7U7vV6ePzxZxg16nfF2+bNm0vnzl0YN+5WHMfhtttur9aaRUSkejnRKKHVloI5MwitWgEeD4GsE0nuPxh/x06asidSP6wyxvw/oAOxhSUAsNaOKmtHhajKMwy41hhzAPAAiYBjjNkFONbapq5WV48cOHCA1NQGxZ+9Xi/hcBi/P/Z179379EP22bNnN5s2fc/DDz/OkiWLeOCB+3j66b9VW80iInVZWdOs33nnbaZNewufz8dVV42if/+BbN26lQcfvJ9IJAzA7bffRfv2HX9+LYUFFC34OjZlb8d2PA3SSB56Dkmn98eX3uhnH19EapW3gI+Irc43CTiZ2DS/MilEVRJrbRe3a5CY1NRU8vPziz87jlMcoI4kPT2dfv0G4PF4OOmkU/j++++qukwRkXrjaNOsc3N38uabU3jhhdcJBoP84Q+j6N27Dy+88CwXXngJgwYNiU/DfpoHHnjkmGsIb99K4ZezKFowD6eoCH/7jjS49EoSTzgRjz+hsi5VRGqXgLX2PmNMCrDeWvtvY8xnwJ/L2lEhqhIZY04GziI2HPiRtXahyyXVS1lZJzBnziyGDj2LZctyOO64svNtr14nMnfuHIYMGcrq1d/SokWLaqhURKR+ONo065Url5OVdQKBQIBAIECbNu1Yu3Y1N910Gw0axGYVRCIRAoGK35vkRKMEVy2ncPZMQqtXgc9P4gknkTRgMAntOlTOxYlIbbbBGNOF2AJxfzHGvA+Ua0haIaqSGGNGA6OAv8c3PWuMecla+5yLZdVLgwadwfz58xgz5hocx+Guu+5hypQ3aNu2HQMGHH7Z/1/96nweffRBrr/+ahzH4b//+65qrlpEpO462jTr0m0pKSns37+fRo1iv2O++24DTz/9OA8++Gi5zxfNz6dw/lwKv5xFdFcu3vRGpAz7JUl9+uFtkFZ5FyYitd1YYI+1do0x5m/AUGB0eXZUiKo8NwCnW2sLAYwx2cBXgEJUNfN6vYwbd3AI6tCh4yH93nzz3eL3gUCAu+66p6pLExGpl442zbp0W35+PmlpsaCzaNECJk58iPHj7y/X/VDhH7ZQMGcGRYvmQyiEv1NnUs89j0BmLzw+X+VelIjUBbdba28FsNZOA6aVd0eFqMrjAUr+F9p7pI4iIiL1ydGmWXfv3pPnn3+GoqIiQqEQGzeup1OnzixatIAnnniUiROfomXLIz/70olECC7PoWDODMLr1oA/gcSTTyW5/yD8rdtWx+WJSO3V2hjTxVq7pqI7KkRVnkeAxfG5lADDKcdNaSIiInVdWdOsL7roUm688Tqi0SjXX/8HEhMTeeKJiYRCISZMiM0SaN++A7fffnfxMaP791E470sK584mumc33sZNSBlxHkmn9cWbkurWpYpI7ZILLIovJhH5caO19sKydvQ4jlOVhdUrxpiuxOZSeoDPrLWrXC6ptAHArLy8A4TDUbdrERERqbDwpu8omD2Dom8WQThMwvGGpH6DCPTIxOPVJJD6KCMjjUkzV5bdsZ66bVB3duzYV63n9Pu9NG6cCjAQmF2tJ68AY8ygw2231s4sa1+NRFUSY8xsa+0A4Fu3axEREalLnHCYopwlFM6eQfi7DRAIkNS7L0n9B+Fv0dLt8kSklipPWDoShajK87kx5rfW2v91uxAREZG6ILp3DwVfzaHwqzk4+/bibZZB6nkXknhKH7zJyW6XJyK1lDFmF+AAAWIzyIriTSnAd9ZaU9YxFKIqzwjgDmPM08TmVHoAx1rb1N2yaob0xikE/FoZ6UiC4Qh78vLL7igiUsc5jkN443oK58ykaOliiEZJ6NaD5P6DSejaTVP2RORns9Y2ATDG/B24zVq7Lf65B3BLeY6hEFV5+gCXAacABcCn1tqP3S2p5gj4fZqvfBS3DerudgkiIq5yQiGKliykYM5MIpu/x5OUTFL/wST3G4ivWYbb5YlI3ZT1Y4ACsNauMMacXp4dFaIqz8tAEvARsZGou4wxg6y1490tS0REpGZyIhHCG9dTtGwpRYvm4xzYj69FS1Iv+A1JJ/fGk5jodokiUrd9ZYx5A/gbkAacB3xXnh0VoipPL2ttrx8/GGNeA5YAClEiIiJx0cJCQt+uIrh8KcFVy3Hy88HnJ9C9B0n9B5HQuSsej8ftMkWkfrgBuBa4CUgA5gG3lWdHhajK840xpoO1dmP8cxJaqU9ERITI7jyCK5YRXJFDaM1qiITxpKQQ6NaTQM9eJHTthjcpye0yRaSesdaGgefirwpRiKo8nYkNCW6Jf24CeI0xCwGstae4VpmIiEg1chyHyJbNBFfkULQ8h8jm7wHwNssguf8gAj2z8HfohMenBYdExD3GmIuBh4DWQD6QDKy31vYsa1+FqMpzqdsFiIiIuMUJhwmtXU1wRQ7BFcuI7s4Djwd/+46knDuSQI8sfM1baKqeiNQkfwbOBO4nNrWvE3BNeXZUiKok1tpy3YQmUhdEo1EmTnyINWtWk5CQwB13jKdt23YH9cnLy+OGG67h1VenkFji5vCNGzdw/fVX8c47Hx20XURqn2j+AYKrVsSm6a1aiVNUCAkBAl27ETj7XALde+JtkOZ2mSJSwxljrgVuBfYBl1trN5RoawpMAVoBU6y1E+Lbrya29sA71trb4tvOAV4DfpwZNs5a+8lRTr3LWrvRGLMIGGqtfdcYc0Z5alaIEpEKmzXrC4LBINnZL7NsWQ6TJ0/ioYceK26fN28uzz33FLt27TpovwMH9jN58iQSEgLVXbKIVJLIzh3Fo02h9WshGsWT1pDAiSeT2COLhOO74tG/4yJSTsaYDOAOoBcwGHgMuKBEl/HAv4FngTnGmHestUuBmcDrQHqJvunAM9ba+8p5+unGmJOIBa/PjDHjgdXl2VEhymVlJO/jgWeAk0s+tNcY8z2wM/5xlrW2XA8FE6ksS5cuoU+fvgBkZmaxatXBzwDzej08/vgzjBr1u+JtjuPw8MN/4frrb+TOO/9YrfWKyLFzolHC32+MBafly4hs+wEAX8tWJJ9xFoEemfjbttdDcEXkWJ0NLLTW5htjPgReMcZ4rLVOvH0EMNJaGzXGvBn/vNRau84Ysx44scSx0oHdFTj3Q8Ruybka+BTYRizElUkhykXlSN7b4+2lhyH3WWtPqpYiRQ7jwIEDpKY2KP7s9XoJh8P4/bH/pPTufehz6l566Xn69h3A8cd3rbY6ReTYOMEgwdW2eMTJ2b8PvF4SjutCUp9+BHpk4mvazO0yRaSGmz59esuxY8d2LLV5t7W2ZNBpBViAeFDKI7ZAW268vTmwNv5+M9D/KKdMB35jjBlF7FFDf7DW7j9K/5eJLSbxIbHnvF5B7HlR/1PGpSlEueyoydtauwdYaIwp3sEYkwAUlXVgY0wjoFHJbdnZ2S2HDBlSieVLfZWamkp+fn7xZ8dxigPUkXz00XQyMprz3nvT2LUrl//6r5t4+um/VXWpIlJO0X17Ca5cTnB5DsHVqyAUwpOURILpQWLPLBJMD7wpKW6XKSK1yCOPPDL1MJvvA+4tta3kUHYa4JRq9xylraRXgfeIha1XgLHAhKP0P9JzXhWiariykvfhpAOtjDGziX3h/mitnXuYfmOBe0puyM7OZsiQITRunFopxUvlysioPTdf9+9/Op9//jmXXnoBS5YsoVs3c9j6fT4vGRlpJCYm8tlnnxZvP/PMM3n99Ve1sISIixzHIbhlC/sWL2b/4sUUrFsHjkNC06Y0HjyYtJNOIsUYPGX8AYmI1Hxu/cYYN27cxWPHjl1QanPp6XZbgD4Axpg0oDGQV6J9G9AFWA4Yflo04hDW2h3Ajvix3gAuLKPEBcaY9iUWiEsi/tu8LPovo/vKSt6l5QK/BhYR+2K8TuyLVdrjxBJ4sdGjR58KTM3LO0A4HD3Weo9JbQoIbtmxY5/bJZTbiSeezqeffsGFF16M4zjcddc9PPXUs7Rt244BAwYX94tEouzYsY/ExOBB+x9pu4hULScSIbRhXWy0aUUO0dzY7bX+tu1JOetcAj2z8LVqjcfjIR/Izytwt2CRctBvjLJV928Mv99L48apDB8+fOvw4cM3lNH9I+B+Y0wKMASYDlxijGltrZ1EbGTpDGPMSmK3vxxxCXJjzG+BfxL7fX0uUDrA/dhvEbHf3A2BeaWe87qtXNdYnk5SZcpK3oeIT/X7Kr7PFOAZY0yCtTZUqt9uDk36bSupbqnnvF4v48bdddC2Dh06HtLvzTffPez+R9ouIpUvWlhAyK6K3d+0cjlOQT74/SR06Ury4KGx+5vSG5V9IBGRKmCt3WmMeQCYR3yhNWJrBHSMd5lAbInzMcA/rLU5AMaYxcRCT7IxZgix39RpwGxi91HNILZA2+H8+ufWrRDlrrKS9yHia9evs9ZuBM4ANpQOUCJSNxzL87gKCgq477672bt3L0lJyYwffz+NGzd26QrELZG8XQRXLIs9v2ntaohE8KSkEuiRSaBnFoGu3fFoOq2I1BDW2peAl0pseqxE2y5i6wiU3udwi6w9F3+Vdb6f/XxXhSgXlZW8jTH3ASOBtHja/guwEsg2xrQltsDEVS6ULiLV4Fiex/Xuu29jTHd+//vreP/9d3n11RcZO/a/3ShfqpHjOEQ2b6JoRQ7B5TlEtmwCwJfRnOQBQwj0zMLfoZOWIRcRqSQKUS4rI3nfQ6nFIeKGVXVdIuK+Y3ke1yWXXE4kEgFg27atNGnSpPoKroXKGu175523mTbtLXw+H1ddNYr+/QcWt/3rX38nNzeXG2642Y3SccIhQmvXEFy+lOCKZUT37AaPB3/HTqSMOI9Ajyz8zVu4UpuISF2nECUiUkMdy/O4AHw+H7fcMoZ169YwadLT1VJrbXW00b7c3J28+eYUXnjhdYLBIH/4wyh69+6D40T561//wooVyxg8+MxqrTd64ADBVctj0/TsSpyiIggECHTtTmDYLwl064G3gW6yFxGpagpRIiI11LE8j+tHTz75HBs3bmDcuFv517+mVVWJtd7RRvtWrlxOVtYJBAIBAoEAbdq0Y+3a1bRp045hw0Zw6qmnsXHjhiqvMbJzB0Xx0abw+rXgOHgbNiTxpFMJ9MgioUtXPAkJVV6HiIj8RCFKpA5Jb5xCwO9zu4waKRiOsCcvv+yONUhW1gnMmTOLoUPPYtmyHI477nBPMzjY66+/TEZGc4YNG0FSUhJer74PR3O00b7SbSkpKezfv5+GDRty2mmn8/77VbPKpBONEv5uQ2w1veXLiGzfCoCvVWuSzzybQM9e+Nu01f1NIiIuUogSqUMCfh+TZq4su2M9dNug7m6XUGGDBp3B/PnzGDPmmuLncU2Z8sYhz+MqacSIkUyYcC/vvTeNaDTKXXeV+dD1eu1oo32l2/Lz80lLq5qpck6wiOC39qdlyPfvA6+XhM7Hk9R3QGwZ8iZNq+TcIiJScQpRIiI11LE8j6tJk6Y89thTVV1anXG00b7u3Xvy/PPPUFRURCgUYuPG9XTq1LnSzh3du4fgymUULV9GaLWFcAhPUjKBbj0I9MwiwfTAm5xcaecTEZHKoxAlIiL1VlmjfRdddCk33ngd0WiU66//A4k/49lKjuMQ2fYDweU5sfubvtsAgLdxE5JO70+gZyYJnbrg8WkKpohITacQJSIi9VZZo30jR57PyJHnH3bfc8/9VZnHdyIRQuvXEFwee/BtdFcuAP52HUgZ9ksCPbLwtWyFx+M59osQEZFqpxAlIiJSiaIFBYTsCoIrlhFctRynoAD8fhKON6SceRaB7pl4G6a7XaaIiPwMClEiIiI/UyRvV3w1vRxCa1dDNIontQGBzBMI9Mgk0LUbnsCxTwUUEZGaRSFKRESkgpxolPDmTcXBKfLDZgB8zVuQPOhMAj2z8LfvqGXIRUTqKIUoEZEK0LO4jq42Po+rvJxQiNDab4sXhoju3QMeD/5OnUn55a9J7JGFL6O522WKiEg1UIgSEakAPYvr6Grj87iOxAmHiOzYQXjTd7H7m75dCcEgBAIETI/YNL3umXhTU90uVUREqplClIiI1GvRwgIi27cR2bY19tftWwlv30Y0dyc4DgDe9EYknXIagR5ZJHQ+Hk9CgstVi4iImxSiRERqKCcaZdb/ZpO7aQNev58hV91EevNWxe0rZn7Eypkf4vH6OGXExXQ4oXdx29JP3iF/z25Ov/BKN0qvcRzHwdm/j3CpsBTZvo3ont0/dfT58DVrjr91G3wnnoK/RUt8LVvha6FlyEVE5CcKUSIiNdT6JfMIh4Kcf+df2bbWMvdfLzPsptgzjfL35LHss/e48O6JhENBpj18J217nIjjRJnx2jNsW/8tx53c1+UrqH5ONEp0d14sIG3bRnj7T4HJyf/pXi1PYiK+5i1I6NIVX/MW+Jq3xN+iJd4mTfWwWxERKZNClIhIDbV19UraZ54MQIvOhu0b1xS3bV+/mpadu+NLSMCXkEDDjFbkbtpAevNWdO07hDbde7F762a3Sq9yTiRCZOeO4tGkyLat8cC0HULB4n6e1Ab4WrQksddJ+Jq3jAWmFi3xpjfSyJKIiBwzhSgRkRoqWJhPIDml+LPX6yUaieD1+Q5pCyQlEyzIJzG1Ae16nsSqOZ+6UXKlc4JFRLZvjwWkbVt/Ck07d0A0WtzP26gxvuYtSTi9C/6SYSm1gYvVi4hIXaUQJSJSQwWSUggWFhR/dqIO3vhUs0BSCsGin9qChQUEUmrvKnHR/ANEtm0jsv0HwttK3K+Ut+unTl4vvqbN8DVvSSCzF77mrfC3aIEvowWeRD3IVkREqo9ClIhIDdWySzc2fDOfLr0HsG2tpUnbDsVtzTsdz9f/foNwKEgkFGL31k00adPexWrL5jgO0T27f1rUocQ9S87+fT91TEiI3a/U8Th8p/WNTcNr0QJf0ww8fv1vS0RE3Kf/G4mI1FCdTjqdTSu+4e2H/gQODLn6Zr75aBrpzVvR8cTTyDzzl0z76104TpTTfv1b/AkBt0sG4os75O6MrYRXPA1vWywsFRUW9/Mkp+Br3oJAj0x8zVvEpuG1aIG3URM8Xm+11BqNRpk48SHWrFlNQkICd9wxnrZt2x3UJy8vjxtuuIZXX51CYokRrxkzPufzzz/h3nv/Ui21iohIzaEQJSJSQ3m8Xgb97oaDtjVu1bb4fY9BZ9Nj0NmH3bdb/6FVWhuANxwmZe8uGuzOpcHuXFL35LLuo/+l6IetEAn/1K9hOr7mLUg89bSfVsJr3hJPWprrizvMmvUFwWCQ7OyXWbYsh8mTJ/HQQ48Vt8+bN5fnnnuKXbt2HbTf448/ytdfz+X447tWd8kiIlIDKESJiMhR+YJFsaC0J5fU3bk02L2T1N25pOzfgyf+MFrH4yG/QToJx3XA29nga9Ey9spogTc52eUrOLKlS5fQp09sKfjMzCxWrVp5ULvX6+Hxx59h1KjfHbQ9K6sXgwYNYdq0/6u2WkVEpOZQiBIREXAcAoX5sRGl3Tvjf40Fp6T8/cXdol4fB9Ibs7dpC37o3JP9jZr+//buPc6qut7/+Gv2XIAZBhhuzshVQb+g4AUVLBQxyySUMu3yq1OalZl5iupnF8+v0+VYWf7Ubr9+P8pjHuucQ2V5yY5p5TGUCsEboPAVkKtynYGBucDMMPv3x94Mw4jixplZc3k9Hw8f7v39rrX2Zy2XM/s937W+i5pBQ6gbMJjmggI+O2Mi27fveY0P6lpqa2spaTWDXyqVoqmpiYLsvVdnnXX2Yde74IILeeqpJZ1SoySp6zFESVJvkk7Tr2b3waBUXdkSmIoaDt6v1FRYRM3AIVQeO7YlKNUOHEJ96SDSnXS/UmcoKSmhrtVDeNPpdEuAkiTp1fibQpJ6oLz9+ynevbPVJXiZwFRSXUVBU2PLcvv6FlM7aAhbjp9A7cAhLYFpX3Ep9IKH0U6efCoLFz7GBRe8jeXLl3H88eOTLkmS1A0YoiSpG8tvbKCkuuqQoNR/1w6Kd+8ilT74MNr6/gOoGTiEneWjMkFp4FBqBw2hsW/XvV+pM8yYcT6LFy/immuuIp1Oc8MNX2X+/F8wcuQozjnnvKTLkyR1UYYoSeqGyjZvICx+lEE7Nre0NeelqBswiJpBQ9k6JlBTlrkEr3bgYPZ3kenPu5pUKsX1199wSNuYMWNfsdzdd//uFW1TppzJlClndlRpkqQuzBAlSd1Iv907CYsfpXz9C9SXlLJqyjnUDBqandyhjHQqP+kSJUnq8QxRktQNFDTsY9wzf2XM80toTuXzwpRzWTfpLJoLCpMuTZKkXscQJUldWF5zMyPjs5zw9GMU7q3npRMms+qMczMTP0iSpEQYoiSpixry0lomLHqE0l07qCofxcqpb2H30PKky5IkqdczRElSF1Oyq5LwxCMM3/QidaWDePot72LrmBN7xZTjkiR1B4YoSeoiCvfWM/7pxxm18mn2FxSy8qyZrD/pDNL5ve9H9cCyYooKnCTjcBqa9lO9s+7IC0qSOkzv+80sSV1M3v79jF75FOOfXkhBYwMbw6msPv0cGvqVJF1aYooK8rltwYqky+iSPjtjYtIlSFKvZ4iSpKSk0wzbuIYJTzxCye6d7Dh2LCunvYWasmFJVyZJkl6DIUqSEtC/ahsTFj3C0M3rqRk4mCffdjnbRx7vfU+SjkpzczO33HITq1evorCwkC996SuMHDmqpf/+++/hvvt+S35+Pldc8VGmTz+Xl19+iW9+82uk02nKyyv4whf+ib59+ya4F1L3YYiSpE5UVF/LCU8+xshVS2ks6sPzZ7+VjRNO8yG5kt6Qxx57lIaGBubN+xnLly/jRz+6jZtuuhWAysod3H33fG6//ec0NDRw7bUf5ayzpvHjH3+fd77zMi688CJ+97t7mT//F1x55ccS3hOpezBESVInSDU1Meb5JYx79m+kmppYf9IZrDntzTT26Zd0aZJ6gKVLn2HatDcBMGnSZFauPHhP4YoVzzF58qkUFRVRVFTEiBGjWLNmFevWreULX3pRErgAACAASURBVHgzAJMnn8oPfnBrIrVL3ZEhSpI6UjrNMesiYfGjFNdUs23UeOLUmdQOHJJ0ZZJ6kNraWkpK+re8T6VSNDU1UVBQ8Iq+4uJiampqGD/+RBYuXMCsWRfz+ON/Ye/e+iRKlwghfAz4DLAH+ECMcV2rviHAfKACmB9jvDHbfiXwFeD+GONns219gZ8DE4FHgU/HGJs7ouZUR2xUr18I4WMhhGUhhL+GEMa26TshhPDHEELl611HUtcxYMdmpv7Xf3D6f9/H/sIiFr/9fTz1tssMUJLaXUlJCXV1B6e+T6fTFBQUHLavrq6O0tJSrrvuszz++F/43Of+kVQqxcCBgzq9bimEMAz4EjAN+Beg7ZDoV4B7gVOA2SGEU7LtC8gEptauAdbFGCcBw4CLO6puQ1SCXsdJsy3bn8phHUkJ61O7h8kLfs+b77+Lkuoqlr/57Sx855VUjhibdGmSeqjJk0/l739fCMDy5cs4/vjxLX0TJ57M0qVPs2/fPmpqali/fi3HHTeOxYv/zkc+cjW33vpD8vJSnHXWtKTKV+92IfBkjLEOeAiYHkJoPcvSbOCR7IjS3dn3xBhfBNa22dZs4JHs618dWLYjeDlfslpOmhDCQ8CdIYS8GGMaIMZYDTwZQnjd60hKTqqpkeOWPcFxyxaRam7mxcnTePHUN9FU1Cfp0iT1cDNmnM/ixYu45pqrSKfT3HDDV5k//xeMHDmKc845j8svfz+f+tTHaW5u5uqrr6VPnz6MHj2Wb3/7GxQVFTJ27Dg+//kvJr0b6mEefPDB8rlz545t07wrxrir1fsKIALEGJtDCDuBwcCBK7GGA2uyr18Cpr/GR7ZsK7vssUdf/WszRCXrSCfNUa8TQhgEHDIuP2/evPKZM2e2W/GSstJpKtY8z4lP/oV+tXvYMjYQz5xJ/QAvjZHUOVKpFNdff8MhbWPGjG15PWfOpcyZc+kh/SefPIl//de2V0NJ7efmm2/+9WGavw58rU1b66vjSoG2gwN5r9HX1oFtvZ5lj5ohKnlHOmmOdp25wFdbN8ybN4+ZM2dSVlaSc5HqeMOGlSZdQo/XEcd40NZNTFj0CIN2bKZ6SDlLz7uEneWjjrxiD+a53PE8xpJykdTPjOuvv/49c+fOXdKmeVeb9y+TuU2FEEIpUAbsbNW/FRgPPAeE7PKv5uXsMqtfx7JviCEqWUc6ad7IOt8D7mzd8IlPfOJM4Nc7d9bS1NQhE5W8Kn/hH9n27Xve8DY8zq+tPY9x35pqwuJHqVi7kr3F/Vl67jt4efwkH5aL53JnaI9jLPUU/rw4ss7+mVFQkKKsrIRZs2ZtmTVr1rojLP4w8I0QQjEwE3gQeG8I4dgY423AA8D5IYQVwHnAVa+xrQeA84HfZ/995xvakddgiErWkU6a17XO4e6Hyl5r2jbpj2yvwqXean99PScsWcDY554A8lh92ptZO3ka+wuLki5NkqRuJ8a4I4TwLWAR2SnOgXcDY7OL3EhmivNrgP+MMS4DCCE8TeaWln4hhJlkBhnmAXeFEJaTmeL8gY6q2xCVoCOdNCGErwNzgNLsifLNGOPdh1lHUgdLNzezb8ki1jz8e8ZVV/PyuJN44Yzz2Nt/QNKlSZLUrcUY7wDuaNV0a6u+KjITq7Vd5/RX2dz72re6wzNEJewIJ81XaXNf06usI6kDNax+gdrf3cP+lzfRb/x4Hjl3DtXDO2zCH0mS1MUZoiTpVezfsZ3aB+6l4bmlpAaVUfrBKxnx1vOofmxl0qVJ6iYGlhVTVJCfdBldUkPTfqp31h15QakLMkRJUhvN9XXU/+kh6hf+BfLzKb7oYvrNOJ+8wiLynDhCUg6KCvK5bcGKpMvokj47Y2LSJUhHzRAlSVnp/fvZu2ghdQ8/SLqulj5nTqPkootJDRiYdGmSJKkLMURJEtAQV1D7u9+yf+sWCsedQMkll1Iwonc/70mSJB2eIUpSr9a0dQu1D9xD48rnSQ0ZSukVH6Po5FO8bE+SJL0qQ5SkXqm5toa6h/+LvX9fSF5RH4ovfhf9ps8gr6Aw6dIkSVIXZ4iS1Kukm5rY+9cF1P3pD6T37qXv2dMpvvAdpPr7xHtJkvT6GKIk9QrpdJp9y5dS+/t7ad6xncITJ1ByybspKK9IujRJktTNGKIk9XilldvY8N37qVuxgvzhxzDgqmsonHCS9z1JkqSjYoiS1GMV1ddywpMLGPnCUvaVlFDyrsvpe/Y55OX74EtJknT0DFGSepxUUxNjnlvCuKV/I9XUxPqTz+TCaz9CVV1z0qVJkqQewBAlqedIpylfu5ITl/yF4ppqto4eTzzrfOoGDmZWSQnU7Um6QkmS1AMYoiT1CAO2b2bioj9Ttu0ldpcN44mL3kfVsWOTLkuSJPVAhihJ3Vqf2t2cuGQBI9Y8x76+xSyf/nY2nXAKpFJJlyYpq7m5mVtuuYnVq1dRWFjIl770FUaOHNXSf//993Dffb8lPz+fK674KNOnn8v3v38Lq1ZFAKqqKunfv5Sf/OTOhPZAkg5liJLULeU3NjB2+RMcv3QReek0L06exppT38T+oj5Jlyapjccee5SGhgbmzfsZy5cv40c/uo2bbroVgMrKHdx993xuv/3nNDQ0cO21H+Wss6bxmc98HoCmpiY++cmP8sUv/q8kd0GSDmGIktS9pNMcu+Y5TlzyF/rW1bB5bOCFs2ZSXzoo6cokvYqlS59h2rQ3ATBp0mRWrlzR0rdixXNMnnwqRUVFFBUVMWLEKNasWcXEiScDcPfd85k69WzGjRufSO2SdDiGKEk5Szc389i/z6Ny0zpSBQXMvOI6Bg4/+NDa5xc8zIoFD5GXyueM2e9hzKlntfQt/dP91FXv4uzLPpzz5w7auokJix5h0I7NVA8t55mZ72RX+ch22SdJHae2tpaSkv4t71OpFE1NTRQUFLyir7i4mJqaGgAaGxu5777f8tOf3tXpNUvSazFEScrZ2mcW0dTYwKVf/g5b10T+9qufcdF1NwBQV72T5Y88wGX/dAtNjQ3c990vM/Kk00inm/nLXT9m69oXOH7Km3L6vH57qjlxyaNUrF3J3uL+LD13Ni+PPxl8WK7ULZSUlFBXV9fyPp1OU1BQcNi+uro6SktLAViyZBGnnTaF/v37I0ldiSFKUs62rFrB6ElTADhmXGDb+tUtfdvWrqJ83ETyCwvJLyxkwLAKKjetY+DwCk5800xGTDyFXVteel2fk9+wj3FL/86Y5xYDeaw+bTprJ09lf2FRR+yWpA4yefKpLFz4GBdc8DaWL1/G8ccfvDRv4sST+clPfsy+fftobGxk/fq1HHfcOACWLHmCs89+c1JlS9KrMkRJylnD3jqK+hW3vE+lUjTv308qP/8VfUV9+9FQX0efkv6MOvl0Vi7885E/oLmZkauWccJTj9GnvpaXxp3MC2fOYF/JgI7YHUkdbMaM81m8eBHXXHMV6XSaG274KvPn/4KRI0dxzjnncfnl7+dTn/o4zc3NXH31tfTpk5kgZsOG9Vx00eyEq5ekVzJEScpZUd9iGvbWt7xPN6dJ5ecf7Nt3sK9hbz1FxSWve9uDX17PhCceYUDVNnYOH8FTb3031cOObb/iJXW6VCrF9dffcEjbmDFjW17PmXMpc+Zc+or1br75+x1dmiQdFUOUpJyVj5/AumcXM/6sc9i6JjJ45JiWvuHHncAT9/6CpsYG9jc2smvLJgaPGH3EbRZXVxEWP8oxG1ZR338Az8ycw5bjJnjfkyRJ6nIMUZJydtzpZ7Pp+We556YvQhpmXvmPPPvwfQwcXsHY06Yy6S0Xc993biCdbmbquz5IwWvcw1Swby/jn1nI6BVP0Zwq4IUzZrDu5DNpLijsxD2SJEl6/QxRknKWl0ox40OfPKStrOLgVOMnzbiQk2ZceNh1J0y/ILON5mZGrXyG8U8/TuG+ejadeAqrppxLQ7GzcEmSpK7NECWp0w3d9CITnniE/rsqqawYzcqpb2HPkGOSLkuSJOl1MURJ6jQlO3cw4YlHGPbSWmoHlPHUBe9m2+jx3vckSZK6FUOUpA5XuLeO8U89zqj4DPsLi1g59XzWTzyDdHZGP0mSpO7EECWpw+Tt38+YFU8y7pm/kt/YwMZwGqunnENj3+IjryypQw0sK6aowD9kvJqGpv1U76xLugxJXZQhSlL7S6cZvmEVYfGjlOzeyfYRx7Fy6luoLRuadGWSsooK8rltwYqky+iyPjtjYtIlSOrCDFGS3rh0mtKq7QzesoHBmzdQtmUjRQ17qRk0hCUXvocdI49PukJJkqR2Y4iSlLt0mv47tzNkczY0bd1I0b69ANSWDmLr2BOpPHYMW8dOIJ1KJVysJElS+zJESTqydJr+O3e0jDQN3rKhJTTVlQ5i2+gTqKoYTVX5aPb2H5BwsZIkSR3LECXpldJp+u/a0RKYBm/ZSNHeegDq+g/MhKby0VRVjGJv/4EJFytJktS5DFGSWoWmjQzekrmnqc/ezKxU9SUD2D5yHJUVo9lZPpr6UkOTJEnq3QxRUm+UTlNSXZkZadqcGWk6GJpK2THyeKrKR1FVMcbQJEmS1IYhSuoN0mlKqqsO3tO0ecOhoWnEcS33NNWXDoS8vIQLliRJ6roMUVJPlE5Tsrsqc3ne5vWZkab6WgD2FvfPhqZR2dA0yNAkSZKUA0OU1AOk02mad2xn53NPcsqCRQzevJG+9TVAJjRVHjsmOxHEaOoMTZIkSW+IIUrqhg6EpsYXV9G4ZjWNa1bRvLsagMH9+mdGmbKX59UNKDM0SZIktSNDlHqcdHMzj/37PCo3rSNVUMDMK65j4PCKlv7nFzzMigUPkZfK54zZ72HMqWdRv2c3f779FpoaGigZNJiZV36awj59EtyLQ6XTaZord9C4ZlXmnxdX01y9C4C80gEUjhtP4fEncMzU0/lxrDI0SZKkbiOE8DHgM8Ae4AMxxnWt+oYA84EKYH6M8cZs+2zg20AT8PEY45MhhAD8HTiw/m0xxrs6omZDVMKO8qT5G1AMNANrYoyXd3bdXdnaZxbR1NjApV/+DlvXRP72q59x0XU3AFBXvZPljzzAZf90C02NDdz33S8z8qTTePKBXzJ+6gwmTL+Apx/8Dc8veIhT3zYnsX1Ip9M0V1UeDE1rVh0MTf1LKRx3Qss/+cOGk5cNTX2GlcILOxOrW5IkKRchhGHAl4BTgPOAW4F3t1rkK8C9wP8FFoYQ7gdWAD8CpgHDgLuAM4CBwL0xxo90dN2GqAQdzUkTY1wKlAKnxBibO7nkbmHLqhWMnjQFgGPGBbatX93St23tKsrHTSS/sJD8wkIGDKugctM6tqxewZR3ZLLoqElTeOKeX3RqiEqn0+yv2pG5NG/1C5mRpl2ZMJQJTZmRpsJxJ5A//JiW0CRJktTNXQg8GWOsCyE8BNwZQsiLMaaz/bOBOTHG5hDC3dn3pcC2GOM2YFsIoSiEUEEmRO3qjKINUck6mpNmKcCRAlQIYRAwqHXbvHnzymfOnNne+9DlNOyto6hfccv7VCpF8/79pPLzX9FX1LcfDfV1NNQfbD/Q1tH67alumXJ89X2301RZCUBeSf/MKNP5b82GpnJDkyRJ6nYefPDB8rlz545t07wrxtg66FQAETLfb0MIO4HBQGW2fziwJvv6JWB663VatR9LJkS9NYTwNLAeuCbGuKX99uggQ1SyjuakARgaQvgjMBT45xjj7w6z7bnAV1s3zJs3j5kzZ1JWVtK+e9HFFPUtpmFvfcv7dHOaVH7+wb59B/sa9tZTVFxCUb9iGvfWU1DUJ9PWr/2PUd+a6uyDbTcwZPMG+tXsztTQpx/9Jp9M8axZlEycSNGxxxqaOsiwYaVJl9AreJw7nse4c3icO57HuHMkdZxvvvnmXx+m+evA19q0pVq9LgXSbfrzDtN3uHX+QOa78wrgZuBG4GO51v16GKKSdzQnzSXAcjLXfv4+hFAeY6xvs973gDtbN3ziE584E/j1zp21NDV17pWAnfk/b/n4Cax7djHjzzqHrWsig0eOaekbftwJPHHvL2hqbGB/YyO7tmxi8IjRlI+bwPplTzJh+gVsXP4UFSec9Ibr6FuzuyU0Dd68geKazOx5DX36UVU+irWTplJVMZqaQUP57HknsX37HnYD7Kg56s/0l9Fr2759zxvehsf4yDzOHc9j3Dk8zh3PY9w52uM456KgIEVZWQnXX3/9e+bOnbukTXfby+1eJnNvEyGEUqAMaH2D91ZgPPAcELLLv5x9fUAAXo4x1gBPZ7d1B/DD9tmjVzJEJetoThpijIuz/Y+HENYBI4DVrdYjO0za9iQd2a7Vd1HHnX42m55/lntu+iKkYeaV/8izD9/HwOEVjD1tKpPecjH3fecG0ulmpr7rgxQUFjHl4vfy33d8nxWP/ZF+/Qdwwcc/l/Pn9q3Z3RKYBm/ZSPGezOFv6NOXqvLRrD/5TCorxlBTNtTZ8yRJUo83a9asLbNmzVp3hMUeBr4RQigGZgIPAu8NIRwbY7wNeAA4P4SwgswcAleRGWkaEkI4hsyVW+tjjFtCCO/NLr8XmAO0DXDtxhCVrJxPmhDCFKApxrg0hDCJzH1P6xKpvovKS6WY8aFPHtJWVnEwP54040JOmnHhIf3FAwYxe+4hVz8eUZ/aPYeMNJUcCE1FfdlZPor1J51BVflo9gweZmiSJEk6jBjjjhDCt4BFZGerJjPR2tjsIjeSma36GuA/Y4zLAEIInwL+SGaK8wOX7DVm244hc9XWlR1VtyEqQUdz0oQQRgLfDyGMA/oAV8YYmzq9+F6oT90eBm/eyODN6xm8ZSMluzODho1FfagqH8WGk6ZkQ9NwQ5MkSdLrFGO8A7ijVdOtrfqqyEzG1nadB8kMQLRuuwe4p4PKPIQhKmG5njQxxk3AZZ1TXe/WEpoOjDS1Ck07jxnFhgmnU1Uxmj1lwyCVOsLWJEmS1FMYoqSsPnU1lG3ZwODNGxmyeQMlu6sAaCwsYmf5KDZOOI2q8tHsHjzc0CRJktSLGaLUaxXV1TB4y8GRpv7VmdDUVFhEVfkoNoZTqKoYY2iSJEnSIQxR6nVGrXyGMc8vof+uzOO4mgqLqDpmJJtOPCVzT9OQY0gbmiRJkvQqDFHqdfrW7qa+ZAAvjZ9MVcUodg8pNzRJkiTpdTNEqddZdcaMpEuQJElSN+af3yVJkiQpB4YoSZIkScqBIUqSJEmScmCIkiRJkqQcGKIkSZIkKQeGKEmSJEnKgSFKkiRJknJgiJIkSZKkHBiiJEmSJCkHhihJkiRJyoEhSpIkSZJyYIiSJEmSpBwYoiRJkiQpB4YoSZIkScqBIUqSJEmScmCIkiRJkqQcGKIkSZIkKQeGKEmSJEnKgSFKkiRJknJgiJIkSZKkHBiiJEmSJCkHhihJkiRJyoEhSpIkSZJyYIiSJEmSpBwYoiRJkiQpB4YoSZIkScqBIUqSJEmScmCIkiRJkqQcGKIkSZIkKQeGKEmSJEnKgSFKkiRJknJgiJIkSZKkHBiiJEmSJCkHhihJkiRJykFB0gX0diGEjwGfAfYAH4gxrmvVNwSYD1QA82OMN2bbZwPfBpqAj8cYn+zsuiVJkqT20F7fh0MIKeBHwAzgOeCKGOPejqjZkagEhRCGAV8CpgH/AtzaZpGvAPcCpwCzQwinhBAKyZwcbwU+BPyk8yqWJEmS2k87fx+eAwyLMU4C1gFXd1TdhqhkXQg8GWOsAx4CpocQ8lr1zwYeiTE2A3dn308FtsUYt8UYnwOKQggVnV24JEmS1A7a8/vwbOCR7Hq/yr7vEF7Ol6wKIALEGJtDCDuBwUBltn84sCb7+iVgeut1WrUfC2xuveEQwiBgUOu2O+64Y9T06dPJz08mOx9b2i+Rz+0uCgra57+Lx/nVeYw7h8e543mMO4fHueN5jDtHex3n1+vAd80///nPI6+99tqxbbp3xRh3tXrfnt+HW7cfaOsQhqjktT6rS4F0m/68w/QdaR2AucBXWzc8/PDDTJ8+nQEDkvlB877Txybyud1FWVlJu2zH4/zqPMadw+Pc8TzGncPj3PE8xp2jvY5zrm6//fb/PEzz14GvtWlrz+/DqcO0tTtDVLJeJnP9JyGEUqAM2NmqfyswnsyNcSG7/MvZ1wccaG/re8CdrRt2797dv7Ky8u1Dhgx5Cmhsn12QJEmSDlG4ZcuWKQMHDnwIqGnTt6vN+/b8Pnyg/U+8+nfkdpGXTndYQNMRhBCGAn8nc6PcBcCVZK7fPDbGeFsI4VbgReDHwCLgKmAF8ALwJjLDm3fGGM/o/OolSZKkN6Y9vw+HEOYAH44xXh5CuAVYF2P8YUfU7cQSCYox7gC+ReaE+DLweWAEcFx2kRvJzDKyFLg3xrgsxtgEfAr4I/BvwMc7u25JkiSpPbTz9+HfAVtCCMuBkXTgLNaOREmSJElSDhyJkiRJkqQcGKIkSZIkKQeGKEmSJEnKgSFKkiRJknJgiJIkSZKkHBiipKwQwqAQwilJ19HThRDGhBAuSLqOniyEMC37rAx1kBBCXgjhkhDCe5OupScLIeQlXUNvEEKYHUL4h6Tr6OlCCKOSrkHtxynOJTIBCpgP7AVOBC6NMcZkq+qZQghnknmI3kPAF2KMexIuqUcJIUwGZgLnkXnGxgc8l9tfCGEI8BbgQ8AYMsf5uWSr6plCCMOBH5J5mOaDSdfTE4UQngPqgQbgJzHGO5OtqGcJIaSAfweKgJOAG2KM9yRbld4oR6LU64UQBgM3ANtijO8C7gRCokX1bDvIPAxvmQGq/cUYlwE/jjFeDjyG53KHiDFWxhh/HWOcA/wBODnpmnqaEEJxCGE6cC9wdtL19FTZ0dQnY4xnAp8BhoYQihMuq8cIIQwEvgTUxxgvA64AKkIIRclWpjeqIOkCpC5gFpm/Dt2afT8CGArcn1hFPdtUYB/wd8hcrhNjdEi8HcUY94cQ+gKFwCQ8l9tdCGE0cDVQDQwHypKtqGcJIfQB7gCeArYAd2dfqx1lR0jmAi8BxBgXA4sTLarneTswDHguhFAUY3wihLA6xtiQdGF6YxyJUq8WQigBTgVWxxifyTa/A7gruap6ruwlUKcBG4FlAAao9tH63pHs6w8DI8lcQqL2t4PMuTwT+AaZEWy1gxDCMcD/A/4cY/wusAtYCVQlWljPlAK+BbwYQng8hDAi6YJ6kux3jLcBFUApsDCEMCLG6LncAxii1NsVkbkMZzlACOHzwIoY4/JEq+q5zgP6krl0pNGbxtvPgTAaQrgC+DmZL/dfiTGuT7KuniaEcHoIoSDGWAd8ENgE1ACNyVbWo4wg86Xz4hDCN4DtwGMxRo9xOwohjIkxNsUYH4gxfpHMcT4v6bp6mCIyv/N+GGP8GpkrMGYmWZDaj5fzqbcbClTEGB/NXj5yA/D+hGvqcUIIxwFNwFhgNY5CtavstfXXAO8GlgI/AGKMsTrRwnqYbOg/cDnq82RuxJ8KDIsxbk+ytp4kxvgUcFEI4RLge2R+dtwBOEFKO8mey2eEEPrEGF/INk8DbkmwrJ5oCDApxrgw+3428B8J1qN2ZIhSb7cDeCGEcBewDXg4xvjHhGvqiS4EbgQqgRtjjHtCCKkYY3PCdfUUKTKX7k0gM6q6ygDV/mKM6RDCHuDXrWYzezHG+HzCpfUoIYTC7KhTE3Af8EcyP5/VTrLnch/gVyGEZ4Hdmeb4eMKl9TRbgGdCCD8j831jdYzxbwnXpHbiFOfq9bKz830S+BPwvDPGdYzscZ4LXAK8O8a4NuGSepzsLFBzgX8APh5jfDTZinqm7L19VwMLgaUxxl0Jl9QjhRDeCfQDfumodcfInsvXAX8FnnFEtf1lf/f9TzI/LxbHGP2DQA9hiJLUqUII+THG/UnX0ZOFEAqAwhhjfdK1SJLUExmiJEmSJCkHzs4nSZIkSTkwREmSJElSDgxRkiRJkpQDQ5QkSZIk5cAQJUlSJwohXBxCWBZC+HTStUiSjo4P25UkqR2EEIqA+cD7Y4wNr7Hop4GPA090SmGSpHbnFOeSJHWiEMJa4HQf0itJ3ZchSpLU7YQQxpAZ9VkDnAosA64Ezgf+N5krLX4cY/xhCOGrQB3wTuB2YB/wL8Bu4Jsxxt+EEKYA/wcoARYCn4kxNoQQ/hv4DfABYDgwK8a46jXq2hljLAshnAdcDaSB6cCvYoxfDCH8M/BPQAQ+CNQCP8lu+3ngEzHG6nY6TJKkDuI9UZKk7up44J9jjJOBvsD/AL4BvAmYDFwSQjgmu+yHgLfGGO8EvgxcBkwDFoQQCoBfkwkwp5AJPte1+pySGOObgV8BH8mhvrOB64FTgOtCCMUxxm8ALwPnxhiXAf8GfD/7uU+RCXeSpC7OECVJ6q5ejjG+mH39EJkRnuOAx4DFZELWqGz/nTHGvdnX/wb8ADgnxrgdOBGojTEuzfb/FHh7q8/5Q/bfTwHlOdS3NMa4Oca4B1gLDG3dGUIoAU4G/utVPleS1EU5sYQkqSfYD+wBFscYP9i6I4QwO9sPQIzxlhDCvcCt2b6fHWZ7h/sjYyOQd5T1HW7dw23LP25KUjfgD2tJUndVFkIYGEIoBP4B+C5wXghhPEAIYdThVgohzIgxrgG+DrwNeAEoDSFMzi7yUeDhji4+xlgDrABmZZs+1hmfK0l64wxRkqTuKgXcBSwF/hpj/CVwFfCbEMJTwE1tV8je/3RpCOFp4JfA12KMjcB7gJ+GEJYChcD3O2kfPgx8Lvu5U8lckihJ6uKcnU+S1O1kZ+e7N8Z4etK1SJJ6H++JkiQpByGES8jMAthafXYGP0lSL+BIlCRJkiTlwHuiJEmSJCkHhihJkiRJyoEhSpIkSZJyYIiSJEmSpBwYoiRJkiQpB4YoSZIkScqBIUqSJEmScmCIkiRJkqQcGKIkSZIkKQeGKEmSJEnKgSFKkiRJknJgiJIklfyQyQAAAEFJREFUSZKkHBiiJEmSJCkHhihJkiRJyoEhSpIkSZJyYIiSJEmSpBwYoiRJkiQpB4YoSZIkScqBIUqSJEmScvD/AS6yYO+U37bRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 根据节点实施分箱 \n",
    "train = combiner.transform(woe_train_data) \n",
    "test = combiner.transform(test_data[woe_train_data.columns]) \n",
    "val = combiner.transform(val_data[woe_train_data.columns]) \n",
    "\n",
    "# 分箱后通过画图观察 \n",
    "from toad.plot import bin_plot, badrate_plot \n",
    "bin_plot(train, x='person_info', target='bad_ind') \n",
    "bin_plot(test, x='person_info', target='bad_ind') \n",
    "bin_plot(val, x='person_info', target='bad_ind')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "id": "283f9097",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<toad.transform.Combiner at 0x2a5f3c08978>"
      ]
     },
     "execution_count": 351,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将第3箱和第4箱合并一下\n",
    "adj_bin = {'person_info': [-0.2610139784946237, -0.1286774193548387, -0.0537175627240143, 0.0626602150537634, 0.078853046594982]}\n",
    "combiner.set_rules(adj_bin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "id": "7fa98c05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a55df5f4e0>"
      ]
     },
     "execution_count": 352,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 根据节点实施分箱 \n",
    "train = combiner.transform(woe_train_data) \n",
    "test = combiner.transform(test_data[woe_train_data.columns]) \n",
    "val = combiner.transform(val_data[woe_train_data.columns]) \n",
    "\n",
    "# 分箱后通过画图观察 \n",
    "from toad.plot import bin_plot, badrate_plot \n",
    "bin_plot(train, x='person_info', target='bad_ind') \n",
    "bin_plot(test, x='person_info', target='bad_ind') \n",
    "bin_plot(val, x='person_info', target='bad_ind')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "id": "50215186",
   "metadata": {},
   "outputs": [],
   "source": [
    "train['samp_type'] = 1\n",
    "train['samp_type'] = 2\n",
    "val['samp_type'] = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "id": "276df5a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_data = pd.concat([train, test, val]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "id": "01e2cfef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a55ee01320>"
      ]
     },
     "execution_count": 355,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "badrate_plot(temp_data, x='samp_type', target='bad_ind', by='person_info')\n",
    "# 大致上还是满足一个单调的情况，就不做修改了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "id": "42d0ef4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a5f3b01da0>"
      ]
     },
     "execution_count": 356,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 分箱后通过画图观察 (act_info)\n",
    "from toad.plot import bin_plot, badrate_plot \n",
    "bin_plot(train, x='act_info', target='bad_ind') \n",
    "bin_plot(test, x='act_info', target='bad_ind') \n",
    "bin_plot(val, x='act_info', target='bad_ind')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "id": "11bfc71e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a5f6116f60>"
      ]
     },
     "execution_count": 357,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['samp_type'] = 1\n",
    "train['samp_type'] = 2\n",
    "val['samp_type'] = 3\n",
    "temp_data = pd.concat([train, test, val]) \n",
    "\n",
    "badrate_plot(temp_data, x='samp_type', target='bad_ind', by='act_info')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "id": "3ef985ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<toad.transform.Combiner at 0x2a5f3c08978>"
      ]
     },
     "execution_count": 358,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 试着将3、4箱合并\n",
    "adj_bin = {'act_info': [0.1153846153846154, 0.2051282051282051, 0.3205128205128205, 0.5256410256410257]}\n",
    "combiner.set_rules(adj_bin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "id": "db0bd646",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a5f7fb7be0>"
      ]
     },
     "execution_count": 359,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GjMmppdsOgqtJGGMSgBSCq1GH7Aa6AV8BJtT/kL8Ds6y18wGstQeBFaG5XgCe+/53cWRaiRJpYHs7dGPhRdfgi4xi4KxptF+3wumQRERERJqKOUB/Y0wsMAKYBVxpjLkr1P4uMNIY4wbOBt4DMMb8Doi01v760ETGmCuNMbGhvuOALxoqaCVRIo2gKLk1Cy+6lty2Hem9YA69Pp+Nq7LS6bBEREREHGWt3Qc8ASwG7gfuBtoR3PoHwS1544BVBCvvrTbGjAF+CfQInX9aYYw5C6gAPgDWA/0JbvVrEK6AKoe1JGcC8/PyivD5/I36wWlpCTwzb22jfmaT5PfTY9k8uqxezP42Waw85xLKY+IcC+eu4T3Zu7fQsc8XERGR5sfrdZOSEgfB5zh95nA4DUIrUSKNye1m/cARfHn2RSTt28XQGS+RuG+n01GJiIiIyHFQEiXigJ1de7HowqsBF4Pf+xeZm75yOiQRERERqSMlUSIOKUxtw8Jx13IgLZNTP30Xs+Rj8DfuNksREREROX5KokQcVB4Tx9LRP2TrKf3pvGYJp3/wOt6yUqfDEhEREZFjUBIl4rCA28PaM0axZtj5tNr5DUNnvER83l6nwxIRERGRo1ASJdJEbDP9WDLmKry+CobMfIX0rRucDklEREREjkBJlEgTkt8miwXjrqUouRUDPnqLris+Bz2GQERERKRJURIl0sSUxSWy+ILxbO/am+4rPqPf3HfwVJQ5HZaIiIiIhCiJEmmC/N4IVg8fy9pB59Dmmw0MmfkKMQV5ToclIiIiIiiJEmm6XC62Zg/ki1FXEFV8kKEzXiZ1e47TUYmIiIi0eEqiRJq43HadWTjuWspi4zl9zmt0XLNU56REREREHKQkSuQkUJKYwqKLrmZ3h+70XDKXPvPew+2rcDosERERkRbJ63QALZ0x5kbgDqAQGG+tzQlrGwf8EkgFplprfxO6vhCIBfzAJmvtDxo7bml8lRFRrDznErquXED3FZ8RfyCX5edeSllcotOhiYiIiLQoWolykDEmDbgPGAz8Gni6RpdTgPOAfsDNxpgeoesJQH9rbX8lUC2My8Wm/sNYfu6lxOXv54wZL5O8e5vTUYmIiIi0KEqinDUKWGatLQZmA8OMMa5DjdbaP1hrS6y1pcByoH1Ym7/Ro5UmY0/HHiy66Gp83ggGzfo3WXal0yGJiIiItBhKopyVCVioSorygFY1OxljvEBfYHXoUmtjzAfGmBXGmIuONLExJtkY0yn89cknn2Q0zG2IEw6mpLFw3AT2Z3Yg+/PZ9FwwB5e/0umwRERERJo9nYlyXngimwAcqezaJGCutXZP6P1FwBrgNOA9Y0yGtbakxpg7gYfDL0yZMoURI0aQkhJXP5GL43xR0Xzx/66gx7JP6bJ6CQn5+1g58mLKY+r+PU5LS2jACEVERESaHyVRztpB8DwUxpgEIIXgalQVY8woYAIw8tA1a+3S0JefGWNygHbAxhpz/wn4Z/iFiRMnng68npdXhM/XuLsB9R/1BuR2s37gSApbpZP92fsMnfESK869jILWdVt43Lu3sIEDFBERkZbE63U3+x/aK4ly1hzgMWNMLDACmAVcaYxpa619xhhzOjAZOMdaexDAGDMA8FlrVxljsoFkIKfmxNbafCC/xuWsBrsTcdzOrr0pSkql/0dvMfi9V1lz5hh2du3ldFgiIiIizY6SKAdZa/cZY54AFhMqcQ5cBnQKdfkvUAy8ZYzxAB8CzwLPGmO6AlHAddZaX2PHLk1TQesMFo6bQL+573DqpzNJ2L+H9acNB7eOP4qIiIjUFyVRDrPWvgC8EHbp6bC29KMMu7xBg5KTWnlMHEtH/4ieiz+ky+rFJOzfw5cjxuGLinY6NBEREZFmQT+eFmmGAh4PX59xPl+dcT6pO7YydObLxOXvczosERERkWZBSZRIM/btKf1YMuYqvOVlDJ05lbRvatYfEREREZHjpSRKpJnLz8hi4bgJFCWmMODDN+mycgEEjlRJX0RERETqQmeiRFqA0vhEFo/9Mdmfv0+P5fNJ3L+b1WeNdTosERHH+P1+nnrqd2zcuIGIiAjuu+8hsrLaV7X/5z+v8uGHcwAYOnQYN9xwMyUlJTz66IMUFBQQHR3DQw89RkpKilO3ICIO0kqUSAvh90awaviFrBs4gjZbNzDk3Vco37vX6bBERBwxf/4nlJeXM2XKi0yadBvPP/9MVdv27duYM+d9/va3F5gy5UWWLl3Exo0bmDnzbYzpyeTJf+e880bx0kv/cPAORMRJWokSaUlcLnL6DOZgShqnfjKDnEceIe7H1xPZ3TgdmYhIo1q1aiWDBw8FIDu7D+vWra1qa9Mmg6eeeg6PxwOAz+cjMjKSK68cT2VlJQC7d++iVatWjR+4iDQJSqJEWqB9WV1YOG4C5y14l4K/TybuwkuJPvNsXC6X06GJNCpt6Wq5ioqKiIuLr3rvdrvx+Xx4vV68Xi/JyckEAgH+8pdn6d7d0KFDRwA8Hg+33z6JzZs38swzf3EqfBFxmLbzibRQxYkpdHroISJ79qZoxpsc/M8rBCoqnA5LpFFpS1fLFRcXR3FxcdX7QCCA13v4Z8tlZWU8+uivKC4u4u6776s29s9//ht/+cvfefDBexstXhFpWpREibRgnpgYEq69kZjzRlO2bAkH/voslQcOOB2WSKOp65auQ6sUh7Z0XXvtDYC2dJ3M+vQ5lUWLPgdgzZrVdOnSraotEAhw//13061bd+6998GqbX1Tp77I+++/B0B0dDRut6fxAxeRJkHb+URaOJfbTdz5Y/G2bUfhtKnkP/sHEq+9kYhOnZ0OTaTBaUtXyzV8+EiWLl3MpEk3EAgEeOCBh5k27RWystpTWeln5crllJeXs2jRAgAmTbqVsWPH8fjjj/Duu9Px+/088MD/OHoPIuIcJVEiAkBUn354WqdT8M//48Df/kz8ZVcSPWio02GJNKi6bOn67W8fIzY29ohburZuzeGee+7gtdemN1rMUj/cbjf33PNAtWsdO3aq+nru3AVHHPf00881ZFgicpLQdj4RqeLNbEvyHb8goktXDr7+Lw6+8zqBUCUqkeZIW7pEROREaCVKRKpxx8aR+JOfUvTedErnf0zlrp0kXHMD7rAtTyLNhbZ0iYjIiXAFAgGnY5DGcyYwPy+vCJ/P36gfnJaWwDPz1tbeURrVXcN7sndv4VHbS79YzME3p+FOSCRxwk1422U1YnQiIiJyMvJ63aSkxAGcBXzmcDgNQtv5ROSook8fTNItd4LfT/5fnqHsy+VOhyQiIiLiOCVRInJMEe07knz7PcHqfa+8SNGsGQT8jbuSKSIiItKU6EyUiNTKnZhI0qTbOPj265TM/QDfzh0kXDUBd0yM06GJSBOUlBJLpFcFN5qScl8lB/KKa+8oInWiJEpE6sTljSD+B1fhbZdF0fQ3OfDcH0m47ma86W2cDk1EmphIr0fnYJuYu4b3dDoEkWZF2/lEpM5cLhcxZwwnaeJt+IuLOfDcHylf+5XTYYmIiIg0KiVRInLcIrp0I/mOe3CntqbgxSkUz52DKn2KiIhIS6HtfCJyQjwprUi+5S4KX3+V4lkz8e3YTsKV43FFRjkdmjQROhfT9OhcjIhI/VASJSInzBUZScL46yhp257iWTPI37ObxOtuwtMq1enQpAnQuZimR+diRETqh7bzicj34nK5iB15Hok3TMSft5/8Z5+kfNMGp8MSERERaTBKokSkXkSe0pvk23+BOz6egv99npLPPtU5KREREWmWlESJSL3xpKWTdNvdRJ7Si6Lpb3Dw9X8R8FU4HZaIiIhIvdKZKBGpV+7oGBIm3ETxnP9S8tFsKnfvInHCjbgTk5wOTURERJogY8yNwB1AITDeWpsT1pYKTAMygWnW2seNMV7gj8BZBBeFbrLWfmGMcQPPA8OBr4AJ1trShohZK1EiUu9cbjdxoy8k4Zob8O3cQf6zT1LxTY7TYYmIiEgTY4xJA+4DBgO/Bp6u0eUh4B2gLzDWGNMX6Ah8Ya09DfgV8Gyo7zggzVqbDeQANzdU3EqiRKTBRPXtT/KtPwePhwOTn6V06SKnQxIREZFGMmvWrAxjTKcar+Qa3UYBy6y1xcBsYJgxxhXWPhaYa631A28AY621m6y1r4TaPwM6hPcNff1a6H2DUBIlIg3K27YdyXfcQ0TnLhx87VUOTn+TQGWl02GJiIhIA3vyySdfB7bUeN1Zo1smYAFCiVIe0CqsPR3YFPp6O9C2xvjBwPKacx2lb71REiUiDc4dF0/ijbcQfeYISj/7hIK/T8ZfVOR0WCIiItKA7rnnniuAzjVefzpC1/CcJAGoWd7XdaS20NmoRwiej6o515HmqTcqLCEijcLl8RB/8eV427bj4Jv/If/PT5I44Sa8bds5HZqIiIg0gDFjxuwaM2ZMTi3ddhBcTcIYkwCkEFyNOmQ30I1goQgT6n/I34FZ1tr5YXMZ4MMj9K1XWokSkUYVPXAISbfcAb4K8p9/mrJVK5wOCb/fz5NPPsHEiddz6603s23bt9Xa//OfV7nppgncdNMEXnjhfwEoKyvlwQfv4ZZbbuQXv7idvLy8I00tIiIixzYH6G+MiQVGALOAK40xd4Xa3wVGhirvnQ28B2CM+R0Qaa39ddhc7wIjQ1+PPNS3ISiJEpFGF9GhE0m334s3sy2FU1+g6P13Cfj9jsUzf/4nlJeXM2XKi0yadBvPP/9MVdv27duYM+d9/va3F5gy5UWWLl3Exo0bePvtN+jSpRuTJ/+d0aPH8tJL/3AsfhERkZOVtXYf8ASwGLgfuBtoR3DrH8DjBKvurQLesdauNsaMAX4J9DDGrAi9zgJmAruMMWuALOB/GypubecTEUd4kpJI+untHHzrteDzpHZuJ/6qa3FHxzR6LKtWrWTw4KEAZGf3Yd26tVVtbdpk8NRTz+HxeADw+XxERkayatWXjB9/LQBDhgzjn/9UEiUiInIirLUvAC+EXXo6rG0/wQp+4f1ncficVE231nuAR6CVKBFxjMsbQfwV44m75AeUr/uaA889TeXePY0eR1FREXFx8VXv3W43Pp8PAK/XS3JyMoFAgOef/xPduxs6dOhIUVER8fHBMbGxsRQVHWz0uEVERMQZSqJExFEul4uYYWeTeNPP8B8sJP/Pf6R83deNGkNcXBzFxcVV7wOBAF7v4YX6srIyHn30VxQXF3H33feFjQlWGCwuLq5KqERERKT5UxIlIk1CZLceJN9xD+6UFApe+BvFH39IINBglUmr6dPnVBYt+hyANWtW06VLt6q2QCDA/fffTbdu3bn33gertvX16XMqCxcGxyxa9Dmnntq/UWIVERER5+lMlIg0GZ5WqSTf+nMK//Mqxf+dTuWObcRfMR5XZGSDfu7w4SNZunQxkybdQCAQ4IEHHmbatFfIympPZaWflSuXU15ezqJFCwCYNOlWLr30Bzz++MP89Kc/ISIigocffrxBYxQREZGmQ0mUiDQprsgoEq6+npKPsyh+/118e3eTOOEmPCmtah98gtxuN/fc80C1ax07dqr6eu7cBUcc9/jjv2+wmERERKTp0nY+EWlyXC4XseeMIvH6m/Hn7iP/2Sep2LzR6bBEREREACVRItKERfbMJum2u3HHxnJgynOULJjfaOekRERERI5GSZSINGne9AySbvsFEaYnRW+/xsE3pxEIlR8XERERcYLORDnMGHMjcAdQCIy31uaEtY0j+DTmVGCqtfY3oetjgd8CPuAma+2yxo5bpDG5Y2JIvO5mime/R8ncOVTu2knitTfiTkx0OjQRERFpgbQS5SBjTBpwHzAY+DVhT2cOOQU4D+gH3GyM6WGMiQCeD12/BvjfxotYxDkut5u4MReRcPX1+HZsJ//PT1Lx7TdOhyUiIiItkJIoZ40Clllri4HZwDBjjOtQo7X2D9baEmttKbAcaA8MAvZYa/dYa78CIo0xmTUnNsYkG2M6hb8++eSTjMa5LZGGE3XqAJJ/dhe43RyY/Ayly5Y4HZKIiIi0MNrO56xMwAJYa/3GmDygFZAb3skY4wX6AquB4YfGhGwH2gI7a8x9J/Bw+IUpU6YwYsQIUlLi6vMe5CSXlpYAuPhmAAAgAElEQVTQoPP7/H687nr+eU1aT9K6PML2yZM5OG0qkXl7SL/ySlyhB+HKsTXI90ROGg39Z16aLn3vReqPkijnhf9PJgE4UumxScBca+0eY0xdx/wJ+Gf4hYkTJ54OvJ6XV4TP5/8+MR83/cXddO3dW9ig86elJfDMvLUNMrdryIWc4o6D2bNZv9ry5chxVETFNMhnNSd3De/Z4N930J/7pkrf+5arMb73IgBer7vZ/9BeSZSzdhA8D4UxJgFIAfLCOxhjRgETgJFhY0x4l9C1aqy1+UB+jctZ9RK1SBMRcHtYO+Q8Clql03vBHIbOeJnl513GwZQ0p0MTERGRZkz7OZw1B+hvjIkFRgCzgCuNMXcBGGNOByYDl1trD4bGLAFSjTFtjDF9gK3W2l2NH7pI07G9R1+WXHAVbl8FQ2ZOJT1nvdMhiYiISDOmJMpB1tp9wBPAYuB+4G6gHdA51OW/BFcL3zLGrDDGPGmt9QE/Az4AXgJuavTARZqg/PR2LBw3gYPJrRkw9226LZ8PejCviIiINABt53OYtfYF4IWwS0+HtaUfZcwsgqtWIhKmLC6BJReMp/eC2XRbuYCE/XtZdfZYKiOinA5NREREmhGtRIlIs+L3ell91gWsHXwuad9uZMjMqcQW5NU+UERERKSOlESJSPPjcrG19+l8cf6VRJUUMXTGS7TettnpqERERKSZ0HY+EWm29rftxMKLJjDgo7c47YM3sKefTU72IHC5qvUL+P3Mf3UKudtycHu9jJhwK0nph59h/fW8OaydNxuX28NpY6+g46kDKczdyycvPYe/shKAs6+5heSMdo16fyIiIuIMrUSJSLNWkpjMoguvZnfH7pyy9BP6znsXt6+iWp8tKxfjqyjn0vt/z5DLrmXhay9WtRUfyGPN3He55Je/Y+ydD7P47alUVlSwdPq/yB55ARff8xsGjPkBi9+a2ti3JiIiIg7RSpSINHuVEZGsHHkJXb5cSI/l84nL38+Kcy+lND4RgF0b1tIhewAAbboa9mzdWDV2z5YNZHTtiSciAk9EBIlpmeRuy2HoFdcTGRMLgN9fiSciovFvTERERByhlSgRaRlcLjb3O4Nl511GXMF+hs54ieRd2wAoLy2uSogA3G531Ta9mm2R0TGUlxQTk5CIx+slf9d2Fr7+T06/6EeNez8iIiLiGCVRItKi7O3QnYUXXYMvMopB7/+b9utWEhkdS3lpSVWfgD+A2+MBCLaVHW4rLy0hMjYOgO3rVvP+X37LOT+5U+ehREREWhAlUSLS4hQlt2bhRdeSm9mR3gtmc3plKd+u+gKA3ZssrbI6VvVN79ydXRu+xldRTllxEfm7ttGqXQe2r1vN59P+ztg7/4f0Tt2cuhURERFxgM5EiUiL5IuKZtn/+wE9ls3jwlWL2LxrD+/85h4CbjcjrruNL+dMJyk9k079BpF9zoVM//0DBAJ+Bl3yY7wRkSz4zz/wV/qY+8KzACRntOPsa25x+K5ERESkMSiJEpGWy+1m/cARFLZK57bPZlEeHcOKcy+joHUGKZlZVd16DR9Fr+Gjqg294uE/NXa0IiIi0kRoO5+ItHg7u/Zi8dgfAzD4vVfJ3PS1wxGJiIhIU6YkSkQEKGidwcJxEzjQOoNTP52JWfIx+P1OhyUiIiJNkJIoEZGQ8pg4lo7+Ed+c0p/Oa5Zw2gdv4C0rdTosERERaWKURImIhAl4PHx9xijWDDuf1J1bGTrzZeLy9jkdloiIiDQhSqJERI5gm+nHkjFX4a0oZ+jMqaRv3eB0SCIiItJEKIkSETmK/DZZLBh3LUXJrRjw0VsM+OAN2uRYXJWVTocmIiIiDlKJcxGRYyiLS2TxBePpsmoRWetXkf7tJsqiY9nRtTfbe/ThYEqa0yGKiIhII1MSJSJSC783go0DzmJTv2Gkbt9C1vpVdPx6GZ2/Wkp+WibbuvdlZ5eeVEZGOR2qiIiINAIlUSIidRRwu9nXviv72nclsqSItpu+ot36VWQvmE3PxR+xq/MpbOvRl7w2WeByOR2uiIiINBAlUSIiJ6A8Jo6c7EHk9B5I0t6dZG1YRebmtbTbuIaixBS2d+/D9m7ZlMUlOB2qiIiIHIUxpj/QwVo7PfQ+2lpb6/NNlESJiHwfLhcH0ttyIL0t6wadQ5uc9WRtWEWPZfPovnw+e9t1ZnuPvuxp342Ax+N0tCIiIhJijHkCyASGANONMVnAZGBcbWOVRImI1JPKiEh2dM9mR/dsYg/sp92GNbTbuJr0ue+oGIWIiEjTc4m1tpcxZjmAtXZbKJGqlZIoEZEGUJzUig2nD2fjgDNVjEJERKRpKjLGpAMBAGPMUKC8LgOVRImINKDwYhQRJcW03fQVWSpGISIi0hRMAmYCXYwx84EM4Id1GagkSkSkkVTExLI1eyBbe59O0r6dZK1XMQoRERGnWGuXGWPOAHoAbsAClXUZqyRKRKSxuVwcSGvLgTQVoxAREXGKMWaOtXYUsDbs2qfA2bWNVRJVT4wxicAdwGlACTAXeMFaW6dsVkRapmrFKAryaLd+tYpRiIiINCBjzCCCFflOMcbcFtaURbBaX62URNWfaQSz2MkElwGvAwYCNzsYk4icRIoTU1SMQkREpOGVABUEc6GUsOvrgZF1mUBJVP3JsNZeEPb+I2PMaseiEZGTVp2KUXTvQ15GexWjEBEROU7W2tXAamPM19baT09kDiVR9WeBMaavtXYVgDEmA1jpcEwicpJTMQoREWnujDE3EjwWUwiMt9bmhLWlEtzxlQlMs9Y+Hrp+HfAQMMNae1fo2vnAy8CO0PB7rLUfHuOj1xljfgV0JFhYAgBr7U9qi1lJVP0ZDdxojCkCXEAUEDDG7AcC1tpUR6MTkZNbWDGKtYPPJWOLVTEKERE56Rlj0oD7gL4ECzo8DVwW1uUh4B3gr8DnxpgZoUWLecBUICmsbxIw2Vr7aB0//i1gDsHqfM8AAwhu86uVkqh6Yq3t5nQMItIy+L0RKkYhIiLNxShgmbW22BgzG/inMcZlrQ2E2scC46y1fmPMG6H3q6y1m40xW4B+YXMlAfnH8dmR1tpHjTGxwBZr7TvGmLnAr2sbqCSqHhljBgD/j+By4Bxr7TKHQxKRZu47xSg2rKLj2lAxitaZbOuhYhQiIuKMWbNmZdx5552dalzOt9aGJzqZBJ/PRChRygNaAbmh9nRgU+jr7cCwY3xkEvBDY8xPCB6rucVae/AY/XOMMd0Ibhf8jTHmv0By7XcWtvdPvh9jzETgb0AZwYoffzXGTHI2KhFpKQ4Vo1h5zqV8/MOfsXbQOXh8FWQvmM05/36ePvPeI2XnNxAI1D6ZiIhIPXjyySdfB7bUeN15hK7hOUkCUPMfK9cx2sK9BFxDMNGKO8pnhbsT2GWtXQH8H3AKMLGWMYBWourTT4Eh1tpSAGPMFGARwcRKRKTR1FaMYl/BOVT27IcnqU4/bBMRETkh99xzzxV33nnnFzUu19xutwMYDGCMSSBYcjwvrH030A34CjAcLhrxHdbavcDe0FyvAJfXEuK91to7QmOnA9Nr6V9FSVT9cQHhp7m1yicizqpZjCLHkrV+FXvffBNcbxFhehI9cCiRvbJxefXPgYiI1K8xY8bsGjNmTE4t3eYAj4XOJY0AZgFXGmPaWmufAd4FRhpj1hIsPHHD0SYyxvwY+A/B/4dfANRM4Gpqa4zpZq3dWJf7Cad/NevPk8CK0F5KgDHU4VCaiEhj8Hsj2NEtmx3dsvmZacWO2R9R9sViCqf+A1dcPFEDBhI9aCjejDo9qF1ERKReWGv3GWOeABYTKnFOsDpfp1CXxwmeWZoE/Dv0jCeMMSsInp2KMcaMILialQB8RvAc1afA5Fo+PhdYHiomURkWU20rWEqi6ou19hVjzBLgXIKrUn+z1q5zOCwRke+IbNOGuDEXETvqAirWr6N06SJKF8yjdP7HeNt3JHrQUCJPHYA7JsbpUEVEpAWw1r4AvBB26emwtv0EK/jVHNP/CFP9jeM7SvOv0Ou4KYmqJ8aYz6y1ZwLrnY5FRKQuXB4PkT17E9mzN/6DhZQtX0rpkkUcfHMazHiTqL79iR44BG+XbrhcrtonFBEROYlYa+ed6FglUfXnY2PMj621rzodiIjI8XLHJxAz/ByizxqJ79utlC1ZRNnKZZQtW4I7tTXRA4cQdfpgFaMQEZGTnjFmP8Eqf5EEd5CVhZpigW+staa2OZRE1Z+xwH3GmL8Q3FPpAgLW2lRnwxIRqTuXy0VEh05EdOhE3LjLKFu9ktIlCyl+/12KZ7+nYhQiInLSs9a2AjDG/Au4y1q7O/S+F3B7XebQv4D1ZzBwFXAawedEfWSt/cDZkERETpwrMpLo0wYRfdogKvftpXTpIhWjEBGR5qTPoQQKwFr7tTFmSF0GKomqPy8C0QTLNFYCDxhjhltrHzrWIGPMjcAdhKqRWGtzwtq6E6wqMiB8RcsY8y2wL/R2vrW2ThmziMiJ8rROCxajOH8sFXatilGIiEhzsCj0PKn/I1jZ72Lgm7oMVBJVf/paa/seemOMeRlYCRw1iTLGpAH3AX0J1r1/mmBJx0P2hNo/rDG08CgVSUREGpTL7a5zMQoREZEm7qfAjcCtQATBMut31WWgkqj686UxpqO1dmvofTS1V+obBSyz1hYbY2YD/zTGuKy1AQBr7QFgmTGHz7YZYyI4fPjtqIwxyUC1E+BTpkzJGDFiRF3vR0TkmL5TjGJp9WIU7hFnE+VtQ1lcgtOhioiIfIe11sfxl0UHlETVp64ElwR3hN63AtzGmGUA1trTjjAmE7Chdr8xJi80LvcYn5MEZBpjPiP4NOa7rbULj9DvTuDh8AtTpkxhxIgRpKTEHcdtSXOXlqb/4LZE9f59T+8Dp/XBX3YthV98Qf68eex9801GuFzsbdeZ7T36sqd9NwIeT/1+rhw3/ZlvufS9F6nOGHMF8DugLVAMxABbrLW9axurJKr+/OgEx7nDvk4gWG7xWHKBS4DlwOXAVOBI+2b+BPwz/MLEiRNPB17PyyvC5/OfYLgnRn9xN1179xY26Pz63jdNDfp979GX2B59yfQXM+fVd2i3cTXpc9+hPDqGHV17s61HXw6mpDXc58sxNfSfedCf+6aqMb73IgBer/tk+aH9r4FzgMcIbu3rDNxQl4FKouqJtbZOh9Bq2EGwqh/GmAQgBcir5XMCwKLQmGnAZGNMhLW2oka/fCC/xvCsE4hRROSERLZpw4bTh7NhwJm03r6FrA2r6LB2OZ2++oL81pls79GXnV164ouMcjpUERFpmfZba7caY5YD51prZxpjRtZloJIoZ80BHjPGxAIjgFnAlcaYttbaZ440IPSN3Rw6ezUSyKmZQImINCluN/vad2Vf+65ElBTTdtNXZK1fRe8Fszll8Ufs6mTY1qMveRntweVyOloREWk5Zhlj+gMvA3ONMQ8BG+oyUEmUg6y1+4wxTxCsBFIIjCdYna8TgDHmUWAckGCMWQH8BlgLTDHGZBEsMDHBgdBFRE5IRUwsW7MHsrX36STt20m79avJ3Pw17TZ9RVFCMtt79GV7t2wVoxARkcbwO4JHcq4DPgJ2E6yWXSslUQ6z1r4AvBB26emwtoepURwiZHRDxyUi0qBcLg6kteVAWlvWDT6HjBxL1vpV9Fg2j+7L56sYhYiINIYXCRaTmE3wOa9XE6xR8D+1DVQSJSIijvJ7I9jRLZsd3bKJLcij3YbVtNvQOMUoAn4/81+dQu62HNxeLyMm3EpSemZV+9fz5rB23mxcbg+njb2CjqcOrGpb9eEMig/kM+Tya+s9LhERaRRHe86rkigRETl5FCemsOG04Wzo3zjFKLasXIyvopxL7/89uzdZFr72IqNvfSAYy4E81sx9l8sffApfRTnT/3A/Wb36EQj4+fTlyezesp4uA4bWSxwiIuKIL4wxHcIKxEUTevxQbZREiYhI09NIxSh2bVhLh+wBALTpatizdWNV254tG8jo2hNPRASeiAgS0zLJ3ZZDUnomPYaOoF3PvuTv2v69b1VERBpXqBpfAEgEFtd4zuvuusyhJEpERJq0hixGUV5aTGRMbNV7t9uNv7ISt8fznbbI6BjKS4qJiounfe/+rPv8o3q5PxERaXSXfN8JlESJiMjJ4QjFKNqtX/29ilFERsdSXlpS9T7gD+AOjY2MjqW87HBbeWkJkbEnxcMjRUTkGE7w+a7VKIkSEZGTTn0Vo8jodgo5Xy6l28Az2b3J0iqrY1VbeufuLHnnFXwV5VRWVJC/axut2nVo6FsTEZGTgJIoERE5qX2fYhSd+w9h29df8vbvfgkBGHHdbXw5ZzpJ6Zl06jeI7HMuZPrvHyAQ8DPokh/jjYh04A5FRKSpURIlIiLNwwkUo3C53Qy/5qfVpknJzKr6utfwUfQaPuqIH3fKsHMb7l5ERKRJUxIlIiLNTq3FKLr3YXv3PidUjEJERERJlIiINF9HK0axfD7dV3zGvnad2XacxShERESURImISItwtGIU/Y+zGIWIiIiSKBERaXG+TzEKERERJVEiItJy1SxGsfm7xSh2dMvmQOsMfFHRTkcrIiJNhJIoERERQsUoeg9ka6/TSdy3i6z1q6qKUQCUxsRRlJRKUXKr4K9JrTiY3JrSuISqan8iItIyKIkSEREJ53JRkJbJ12mZrBt8Dqk7thJ3IJf4/FziDuSSuXktEeVlVd193giKkloFX8mpHExKpSgpleLEFPxe/TMrItIc6W93ERGRo/B7I9jboRt76Xb4YiBAZGkxcQf2E5efS/yBXOLyc0nes4PMzWs5tCYVcLkojk+iKDk1lGSFEqzkVCqiYxy5HxERqR9KokRERI6Hy0V5TBzlMXHBB/eGcfsqgsnVgVzi84O/xh3YT+qOrXgqfVX9yqNjQitW1VevSuITwe1u7DsSEZHjpCRKRESknvi9ERSmtqEwtU2NBj8xRQXE5QeTqkNbA9O/2UjU+lVV3So9HooTW3EwfPUq9LXfG9HIdyMiIkejJEpERKShud2UJCRTkpDMvvZdqzVFlJaEVqwOr14l5u4mI8fiCgSq+pXEJ3539So5lfLoWBW2EBFpZEqiREREHFQRHUN+dBb5bbKqXXf7fMQW5h1evQqdvUrZvQ2vr+Lw+MiosBWrVA6GqgeWJCQT0NZAEZEGoSRKRESkCfJ7vRxMSeNgSlr1hkCA6KLCqvNWh7YGtt62hawNqw+Pd7spSkwJlWMPlmYvaR+N3xuPO1rPvAr4/cx/dQq523Jwe72MmHArSemZVe1fz5vD2nmzcbk9nDb2CjqeOrCqbdWHMyg+kM+Qy691InQRaQKURImIiJxMXC5K4xMpjU8kt13nak3e8rLQylUu8YeqB+bvI/2bDbgDAXLmvQeAOykZT1o6nvQ2eNIzQr+2wZ2YhKuFbA3csnIxvopyLr3/9+zeZFn42ouMvvUBAIoP5LFm7rtc/uBT+CrKmf6H+8nq1Y9AwM+nL09m95b1dBkw1OE7EBEnKYkSERFpJnyRURxIb8uB9LbVrrsqK4ktzOeqdjHs35hD5Z7dVO7ZTdnypQRKSw/3i4rGk56OJ61NVWLlSc/Ak9oaVzN75tWuDWvpkD0AgDZdDXu2bqxq27NlAxlde+KJiMATEUFiWia523JISs+kx9ARtOvZl/xd250KXUSagOb1N6KIiIh8R8DjoSg5lYTTelLaocfh64EAgcICfHt2U7lnF5V79lC5ZzcVmzdStnzp4QncbjyprWskV6HVq5hYB+7o+ysvLSYyLHa3242/shK3x/OdtsjoGMpLiomKi6d97/6s+/wjJ0IWkSZESZSIiEgL5XK5cCUmEZmYBN16VGsLlJVRuXd3KMHaTeXe3VTu3k25XQthz7xyxSfgbZNRLcGqcHWBQKBJVw2MjI6lvLSk6n3AH8Dt8RxuKzvcVl5aQmRsXKPHKCJNl5IoERER+Q5XVBTerA54szpUux6orMSft5/KPbvx7dl1eGvgl8sJlBQDUACc540IPeuqVbXqgcWJKfibwNbAjG6nkPPlUroNPJPdmyytsjpWtaV37s6Sd17BV1FOZUUF+bu20apdh2PMJiItjfN/i4mIiMhJw+Xx4Gmdhqd1GpG9squuBwIBAkUHqdyzm5iSAyxauoa4A/tJ3rODzM1rObQmFQBKEpI5GHreVVHSoWdetaIiuvG2BnbuP4RtX3/J27/7JQRgxHW38eWc6SSlZ9Kp3yCyz7mQ6b9/gEDAz6BLfow3IrLRYhORpk9JlIiIiHxvLpcLV3wC7vgEUtISWOduU9Xm9lUQdyAv7KHCwfLsqTu/wRO2NbA8OubwA4XDVq9K4hOhnp955XK7GX7NT6tdS8k8/KyuXsNH0Wv4qCOOPWXYufUai4icfJREiYiISIPyeyMoTE2nMDW9ekMgQMzBgmBylZ8bfKDwgf2kf7ORqNJVVd0qPR6KE1tVX71KDm4NrNQKkYg4QEmUiIiIOMPloiQhiZKEJPZldanWFFFWEnrm1f7Q6tV+EvfvIWPrelyBQFW/krjE0IpV9QSrPDq2SRe2EJGTm5IoERERaXIqomLIb5NFfpusatddlT7iCvKCydWh1av8/aTsXoXXV3F4fGRUKKFqRVFS69CvqZQkJBOo562BItLyKIkSERGRk0bA4+VgShoHU9JqNASILioMnbs6lGDtp/W2HLI2rKnq5ne7KU5MCZ29Cha0OFTYojIiqpHvRkROVkqiRERE5OTnclEan0hpfCK57TpXa/KWl4XOXe2vKmwRn7+P9G824g74q/qVxsaHEqrg9sADrTM5kJapbYEi8h1KokRERKRZ80VGcSCtLQfS2la77vJXEluYHzx7lb8/VNgil7Yb1xBRUQ7A/oz2bOw/jP0ZHZRMiTQQY8yNwB1AITDeWpsT1pYKTAMygWnW2sdD168DHgJmWGvvCl2LBqYCPYFPgNuttYd/UlKPlESJiIhIixRwe4Jb+pJSoWN4Q4CokiLa5Fi6rFrEoFnTlEyJNBBjTBpwH9AXOBt4GrgsrMtDwDvAX4HPjTEzrLWrgHkEE6aksL6TgBxr7RXGmP8AFwIzGiJunawUERERCedyURYbzze9TmPeDyby9ZD/R2xBHoNmTWPQrH/TaudWpyMUaU5GAcustcXAbGCYMSb8JxVjgbmhFaU3Qu+x1m4GttSYaywwN/T1a4f6NgStRImIiIgchd/r5ZteA9jWoy9Z67+ssTJ1JvszOzgdokiTNWvWrIw777yzU43L+dba/LD3mYAFsNb6jTF5QCsgN9SeDmwKfb0dGHaMj6yaK9S37TH6fi9KokRERERqEUymTmNbj1PDkql////27j3KqvLM8/iXorgVoCKiIHdvjxdUSgWMpQSjbUuItslEk5WeJE7i3ZmRTJbjZSZjTNuJHdprXOkmcYhtkpaOrqhpE1qMjpc2rRGicvUNEEHUIIKAICBVFPPHOYVVZXHZRdXZp6q+n7VcnvO+e+96ztp1DudX7/vubZiSdmPatGkPttB8M/DtZm2NZ8f1B3Y06++2m77mGo61N9u2mtP5JEmS9lJDmCpM8zubqg3rGD/rAcb95gEO/PMbeZcnlZVrr732QmB0s//ubLbZ20AARER/YACwrlH/O8ARxcdR3H5Xdh5rL7bdJ45ESZIkZfSxkalXCyNTawePYGl1DescmZKYPHnyqsmTJy/fw2azge9ERBUwCZgFXBQRh6aU7gAeA86MiMUULjzxtd0c6zHgTODXxf/ft08vYDcMUZIkSa3UUpiaYJiS9lpKaU1EfBd4keIlzilcnW9UcZNbKFzi/ArggZTSfICIeJnC2qk+ETEJmABMB+6PiAUULnH+WHvVbYiSJEnaRx+FqRMYnl5l9LwXC2FqyAiWVp/OusHD8y5RKlsppRnAjEZNtzfqe4/CFfya71O9i8N9oW2ra5khKmd7uLnYkcAPgZNSSgP3Zh9JkpSf+soerDjuFFbGicUw9QITfvPPhimpkzFE5Wgvbi62utj/2wz7SJKknO06TI0sTPMzTEkdmlfny9duby6WUtqQUpqbZZ8GEXFARIxq/N/TTz89uB1fiyRJaqYhTD174eUsnnAWfdevYcJv/plxs2YyYNXKvMuT1EqOROVrTzcX25d9pgI3NW6YPn06kyZNYsCAvm1UvjqDQYP6512CcuB577o89/loOjL1SmHNVIlHpjz3UtsxROVvTzcXa+0+d9Lsso6XX375KcCD69Z9QF1dfcYy940f3OXr3Xc3tuvxPfflqb3PO3juy5XnPl+FMDWOlTGW4ekVDiuGqTVDRrK0+nTWDx7Wbj+7FOdeAqisrOj0f7Q3ROXrbQqXY9zVzcVavU9KaT2wvllz+30yS5KkvdZSmDr1Nz8vSZiStO9cE5Wv2UB1CzcX+0aWfVJKezN6JUmSykxDmHrmwstZPP5T9F+/hlN/83NOmTWTA1a9mXd5knbBkagc7enmYhFxM3A+0L94Q7G/TSk91MI+kiSpA6uv7MGKMeNYefRYhr/2CofNL45MHVocmTrEkSmpnBiicraHm4vdRLOLQ+xiH0mS1Ak0DlMjXnuZ0fNe5NRfG6akcmOIkiRJKjP1lT1YPmY8bxxd3SxMjWJpdY1hSsqZIUqSJKlMGaak8mSIkiRJKnMNYWplFNZMjZ5vmJLyZIiSJEnqILb36Mny48fvvADFzjA1dFRhzdTBQ/MuUeoSDFGSJEkdTNMw9TKj5/+eUx/7mWFKKhFDlCRJUgdVCFMTWHl0dZMw9e7Q0SyrrjFMSe3EECVJktTBNQ9Th81/sUmYgmPyLlHqVAxRkiRJncSuwtQby1+hctI59Bg5Ou8SpU7BECVJktTJNA9Tx702l+333E6POIaqv5hsmJL2UUXeBUiSJKl9NISpI6ZNo+rTf0XdmyvZcM/tbLj3h9S+sTzv8mcVSF0AABQoSURBVKQOy5EoSZKkTq6id2+qzjybPqedwZbfPceWZ55kww9uo8fRxxZGpkaMyrtEqUMxREmSJHUR3Xr1ahSmnmXL04YpqTUMUZIkSV1MIUz9BX1Om2iYklrBECVJktRFGaak1jFESZIkdXENYar3J85g6++eZcszT3XaMFVfX89tt93K0qVL6NGjB9df/y2GDRu+s/9Xv3qYRx/9Jd27d+erX/06NTVncNddt7FkSQLgvffW0q9ff370o/tyegUqB4YoSZIkAcULUHzqHHqfNrEYpoojU8ccVwhTw0fmXeI+e+65p9m2bRvTp/+EBQvmc889d3DrrbcDsHbtGh56aCb33vtTtm3bxlVXfZ1x4yZwzTXfBKCuro4rr/w61133v/N8CSoDXuJckiRJTTSEqQE33EzV5POoW/E6G+7+ezbM+EdqV67Iu7x9Mm/eK0yY8AkAxow5ntdeW7yzb/HihRx//In07NmTfv36MXTocJYtW7Kz/6GHZjJ+/KkcfvgRJa9b5cWRKEmSJLWoxZGpu/++Q49MffDBB/Tt22/n84qKCurq6qisrPxYX1VVFZs2bQKgtraWRx/9JT/+8f0lr1nlxxAlSZKk3fooTJ3B1uefZcuzTxXD1JhimBqRd4l7rW/fvmzevHnn8x07dlBZWdli3+bNm+nfvz8Ac+a8yNixJ9GvXz8kp/NJkiRpr1T07kPVWX/JgBu+TdW5n6FuxZ/YcPc0NsyYTu3KN/Iub68cf/yJvPDC8wAsWDCfww77aGreMcccx7x5L/Phhx+yadMmVqx4ndGjDwdgzpzfc+qpp+VSs8qPI1GSJEnKpCFM9a6ZWBiZeuYpNtw9jZ7HFkamKoeV78jUxIln8tJLL3LFFV9jx44d3HjjTcyc+TOGDRvO6ad/ks9//otcffWl1NfXc9llV9GrVy8A3nhjBeeeOyXn6lUuDFGSJElqlZbC1Pq7yjtMVVRUcO21NzZpGzly1M7H55//Wc4//7Mf22/atLvauzR1IIYoSZIk7ZOOGKakfWGIkiRJUpvYGaZOm8jW559hy7MNYer4YpgavueDSB2AIUqSJEltqqJPH6rOPpfeNZ9sFKa+b5hSp2GIkiRJUrvYZZg6rhimhhqm1DEZoiRJktSumoSpf3+GLc89xfo7DVPquAxRkiRJ2mf7D6iiZ2X3PWzVH750IdsvmMJ7TzzBe48/zvo7v0+/6moGXXABvUeOLEmtXcm2uu1sWLd5zxsqE0OUJEmS9lnPyu7c8ezivd9hwFFUfnYEIxfNZdTCl9j08su8M+JIllbXsHHgIe1XaBfzjYnH5F1Cp2SIkiRJUi7qevVmWXUNK449eWeYqnl0iWFKZc8QJUmSpFw1DVNzGLVwjmFKZc0QJUmSpLJQCFOns+LYU5qEqVUjj2LZ2NMMUyobhihJkiSVlZbC1OAVfyyGqRo2Djw47xLVxRmiJEmSVJYah6lRC+cwcuFL+xymdtTX89zPp7P2zeVUVFYy6av/lf0PHrKzf9Gzs1n87ON0q+jOyVMuZOSJ49iy8X2evPc26rZto+8BBzLp4v9Oj1692vKlqoOpyLsASZIkaXfqevVm6Umn88xFV7J0bA0D315OzaM/YeyTD9N/7epMx3r9lRepq93GZ2/4O0793Ff4j1/8ZGff5g3rWPDUY1xw3a1MmXoTLz78U7bX1jL3sX/hiPETueC673HQiMNY9Ozjbf0S1cEYoiRJktQhNA1TpzUJU/3e27swtWrJYkaMOQmAQw4PVq9YurNv9etLGHz4MXTv0YNeVX3Zb9AQ1r65nFVLP9pn+JiTeGvxq23/4tShOJ1PkiRJHUohTJ3B8uPGMWrhS4xsWDM16iiWjq1h04G7nua3betmevap2vm8oqKC+u3bqeje/WN9PXv3YduWzWzb8lF7Q5u6NkOUJEmSOqQWw9Ty3Yepnr2r2LZ1y87nO+p3UNG9+0d9H37Ut23rFnpW9aVnnypqt26hsmevQlufvu3/4rqQiLgEuAbYCHwppbS8Ud9AYCYwBJiZUrql2D4F+B5QB1yaUpobEQG8ADTsf0dK6f72qNkQJUmSpA6tIUytOO4URi4sXs1v+R9ZNSqKYWrQzm0HH3E0y199iSPGnc47yxIHDhu5s+/g0Ufy+0d+Rl3tNrbX1rJ+1ZscOHQEgw8/mhXz53J0zVmsXPAHhhx5bB4vs1OKiEHA9cAJwCeB24HPNdrkW8AjwD8Az0fEr4DFwD3ABGAQcD9wMrA/8EhK6b+0d92GKEmSJHUKtb36tBCmUpMwNbr6VN5c9CoP33od7IBJF/83Xp39KPsfPIRRY8cz5lOf4dG/u5EdO+oZf8FfU9mjJyd95iL+34y7WPzcE/Tptx9nXfo/8n6pnck5wNyU0uaIeBy4LyK6pZR2FPunAOenlOoj4qHi8/7A6pTSamB1RPSMiCEUQtT6UhRtiJIkSVKn0iRMLXiJUYvmNglTE798ZZPtBwwZtvPxsRPP4diJ5zTpr9rvAKZMvakktXcms2bNGjx16tRRzZrXp5QaB50hQAIoBqV1wIHA2mL/wcCy4uO3gJrG+zRqP5RCiDo7Il4GVgBXpJRWtd0r+oghSpIkSZ1Sba8+LD15IivGjGsSpv48KlhWXcOmAYP2fBC12rRp0x5soflm4NvN2hpfMbw/sKNZf7cW+lra598oBK7FwDTgFuCSrHXvDUNUzlq5kO4/gCqgHliWUvp8qeuWJEnqKHaGqYYLUCyayxDDVLu79tprL5w6deqcZs3Np9u9TWFtExHRHxgArGvU/w5wBLAQiOL2bxcfNwjg7ZTSJuDl4rFmAD9om1fycYaoHLVmIV1KaR6FtH1CSqm+xCVLkiR1WLW9+7Dk5ImNruZXnOY3+miWjT3NMNXGJk+evGry5MnL97DZbOA7EVEFTAJmARdFxKEppTuAx4AzI2Ixhe/LX6Mw0jQwIg6hMN1vRUppVURcVNx+K3A+0DzAtRlDVL5as5BuHhTmjO7uwBFxAHBA47bp06cPnjRpUlu/BkmSpA6lxTD1+muGqRyklNZExHeBFynOzKIwqDCquMktFGZmXQE8kFKaDxARVwNPULjEecOUvdpi2yHAAuDi9qrbEJWv1iykAzgoIp4ADgL+T0rpX1s49lSgyQrI6dOnM2nSJAYM8N4G+sigQf3zLkE58Lx3XZ77rstz/3FNwtSCwjS/wa+/xtLq01lWXbPnA3QQ5X7uU0ozgBmNmm5v1PcehYGH5vvMojBq1bjtYeDhdiqzCUNU/lqzkO48Cun6ZODXETE4pbSl2X53Avc1brj88stPAR5ct+4D6upKOxOw3N+8Xdm7725s1+N77stTe5938NyXK8991+Xn/a7V9u7DklMmsnzMOEYumsPWvv3yLqlNleJ931hlZUWn/6O9ISpfrVlIR0rppWL/v0fEcmAosLTxgYuXjmy+cG8YkiRJalFt78Kl0aU9qdjzJmpHs4HqFhbSfaPY37CQroLCQrpfR8RJEXECQESMobDuaXmpC5ckSZK6KkNUjlJKa4CGhXQ3AN+kMKo0urjJLRSuLDIPeKS4kG41cFNEvAI8CFycUqorde2SJElSV+V0vpxlXUiXUnoT+E+lqU6SJElSc45ESZIkSVIGhihJkiRJysAQJUmSJEkZGKIkSZIkKQNDlCRJkiRlYIiSJEmSpAwMUZIkSZKUgSFKkiRJkjIwREmSJElSBoYoSZIkScrAECVJkiRJGRiiJEmSJCkDQ5QkSZIkZWCIkiRJkqQMDFGSJEmSlIEhSpIkSZIyMERJkiRJUgaGKEmSJEnKwBAlSZIkSRkYoiRJkiQpA0OUJEmSJGVgiJIkSZKkDAxRkiRJkpSBIUqSJEmSMjBESZIkSVIGhihJkiRJysAQJUmSJEkZGKIkSZIkKQNDlCRJkiRlYIiSJEmSpAwMUZIkSZKUgSFKkiRJkjIwREmSJElSBoYoSZIkScrAECVJkiRJGRiiJEmSJCkDQ5QkSZIkZWCIkiRJkqQMDFGSJEmSlIEhSpIkSZIyMERJkiRJUgaVeRfQ1UXEJcA1wEbgSyml5Y36BgIzgSHAzJTSLcX2KcD3gDrg0pTS3FLXLUmSJLWFtvo+HBEVwD3ARGAh8NWU0tb2qNmRqBxFxCDgemAC8DfA7c02+RbwCHACMCUiToiIHhR+Oc4Gvgz8qHQVS5IkSW2njb8Pnw8MSimNAZYDl7VX3YaofJ0DzE0pbQYeB2oioluj/inAUymleuCh4vPxwOqU0uqU0kKgZ0QMKXXhkiRJUhtoy+/DU4Cnivv9ovi8XTidL19DgASQUqqPiHXAgcDaYv/BwLLi47eAmsb7NGo/FPhz4wNHxAHAAY3bZsyYMbympobu3fPJzof275PLz9XuVVa2/++D5778lOK8g+e+HHnuuy4/77uuUr3vGzR813zyySeHXXXVVaOada9PKa1v9Lwtvw83bm9oaxeGqPw1/q3uD+xo1t+thb497QMwFbipccPs2bOpqalhv/3y+YD7QvWoXH6udm/AgL7t/jM89+WnFOcdPPflyHPfdfl533WV6n3f3L333vtAC803A99u1taW34crWmhrc4aofL1NYf4nEdEfGACsa9T/DnAEhYVxUdz+7eLjBg3tzd0J3Ne44f333++3du3avxw4cOAfgNq2eQmSJElSEz1WrVp10v777/84sKlZ3/pmz9vy+3BD+2/Z9XfkNtFtx452C2jag4g4CHiBwkK5s4CLKczfPDSldEdE3A78Cfgh8CLwNWAx8EfgExSGN+9LKZ1c+uolSZKkfdOW34cj4nzgKymlz0fEbcDylNIP2qNuLyyRo5TSGuC7FH4hbgC+CQwFRhc3uYXCVUbmAY+klOanlOqAq4EngH8CLi113ZIkSVJbaOPvw/8KrIqIBcAw2vEq1o5ESZIkSVIGjkRJkiRJUgaGKEmSJEnKwBAlSZIkSRkYoiRJkiQpA0OUJEmSJGVgiJLaQEQcEBEn5F2HSi8iRkbEWXnXodKLiAnFe5Koi4iIbhFxXkRclHctKr2I6JZ3DSofXuJc2kcRcQAwE9gKHAV8NqWU8q1KpRIRp1C4KeDjwP9MKW3MuSSVQEQcD0wCPknhXiZf8n3f+UXEQOBTwJeBkRTO+8J8q1IpRcTBwA8o3Nx1Vt71KD+OREn7ICIOBG4EVqeULgDuAyLXolRqayjc3G++AarrSCnNB36YUvo88By+77uElNLalNKDKaXzgX8Djsu7JpVGRFRFRA3wCHBq3vUof5V5FyB1cJOBnsDtxedDgYOAX+VWkUptPPAh8AIUpnuklBzi7wJSStsjojfQAxiD7/tOLyJGAJcBG4CDgQH5VqRSiIhewAzgD8Aq4KHiY3VhjkRJrRQRfYETgaUppVeKzZ8G7s+vKpVScWrPWGAlMB/AANW5NV4TUXz8FWAY8PPcilIpraHwnp8EfIfC7AN1YhFxCPCPwJMppe8D64HXgPdyLUy5M0RJrdeTwlSOBQAR8U1gcUppQa5VqZQ+CfQG5qaUal103Pk1hOSI+CrwUwpfpr+VUlqRZ11qXxFRHRGVKaXNwF8DbwKbgNp8K1MJDAWGAJ+JiO8A7wLPpZQ8912c0/mk1jsIGJJSero41H8j8MWca1IJRMRooA4YBSzFUaguISJ6AlcAnwPmAXcDKaW0IdfC1K6KfxxpmLa7CNhSfD4opfRunrWp/aWU/gCcGxHnAXdS+OyfAXghmS7OkSip9dYAf4yI+4G/BWanlJ7IuSaVxjkU5sNfBryfUtoYEX6edn4VFKbuHV18vMQA1fkV/ziyEXgwIn4B/Bj4U0ppUb6VqRQiokfxYR3wKDAVWJ1fRSoXXuJc2gfFq/NdCfwWWOTV2bqO4rmfCpwHfC6l9HrOJalEImJ/Cuf+PwOXppSezrcilUJxDeRlwPPAvJTS+pxLUglFxF8BfYB/cdaBwBAlSfskIrqnlLbnXYdKLyIqgR4ppS151yJJKi1DlCRJkiRl4Bx+SZIkScrAECVJkiRJGRiiJEmSJCkDQ5QkSZIkZWCIkiSpjUTEuIh4NSK+l3ctkqT2Y4iSJHV5EXHZXmxzf0QM3sNmlwLfA/5XmxQmSSpLhihJUpcWEVUUbpq9Wymlr6SUVu1hsyOB11JK9W1SnCSpLHmfKElSp1S8Ge7twARgf+Ay4PfA3cAngPXAF4BfAicACbg6pfS7XRzvZeCC4tP/CywBzgLmpJS+FBGXAHcAbwHXAHOBHwOHF9suSSm91favVJJUao5ESZI6pZRSHfBPKaUJwBXAt4Hrgc0ppeOBLwJ/phCkUkqpelcBqgUnUQhjxwGnRcRRKaV7gTnAF1JKj1MIVE+llE4AfgL8Q9u9OklSngxRkqTObFVE3AhcDYwEPk0h/JBSeiul1NrpGCtSSotTSrXAAqCltVLnAA8Uf9YvgPER4b+7ktQJ+GEuSeqUImIk8BiFKXxXU/g3rzvQ1vPYa4Fue7Fdt73cTpJU5gxRkqTO6hRgWUrptxTWPAE8CVwFEBEHR0R/YDUwsB1GiX5LYcogEXEhhbVT29v4Z0iScmCIkiR1Vk8B+0fESxQuLrER+BtgWEQsAB4EhqSUtlAIPIsi4tw2/PlTgXMi4lXgEuDyNjy2JClHXp1PkiRJkjKozLsASZLKRUScDNzbQteZKaX1pa5HklSeHImSJEmSpAxcEyVJkiRJGRiiJEmSJCkDQ5QkSZIkZWCIkiRJkqQMDFGSJEmSlIEhSpIkSZIyMERJkiRJUgaGKEmSJEnKwBAlSZIkSRkYoiRJkiQpA0OUJEmSJGVgiJIkSZKkDAxRkiRJkpSBIUqSJEmSMjBESZIkSVIGhihJkiRJysAQJUmSJEkZGKIkSZIkKQNDlCRJkiRl8P8B2J2ry04qj1AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 根据节点实施分箱 \n",
    "train = combiner.transform(woe_train_data) \n",
    "test = combiner.transform(test_data[woe_train_data.columns]) \n",
    "val = combiner.transform(val_data[woe_train_data.columns]) \n",
    "\n",
    "# 分箱后通过画图观察 \n",
    "from toad.plot import bin_plot, badrate_plot \n",
    "bin_plot(train, x='act_info', target='bad_ind') \n",
    "bin_plot(test, x='act_info', target='bad_ind') \n",
    "bin_plot(val, x='act_info', target='bad_ind')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "id": "a87c78a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a5f81bd1d0>"
      ]
     },
     "execution_count": 360,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['samp_type'] = 1\n",
    "train['samp_type'] = 2\n",
    "val['samp_type'] = 3\n",
    "temp_data = pd.concat([train, test, val]) \n",
    "\n",
    "badrate_plot(temp_data, x='samp_type', target='bad_ind', by='act_info')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "id": "b7d35191",
   "metadata": {},
   "outputs": [],
   "source": [
    "# WOE编码\n",
    "t = toad.transform.WOETransformer() \n",
    "train_data_woe = t.fit_transform(train[feature_list], train['bad_ind']) \n",
    "test_data_woe = t.transform(test[feature_list]) \n",
    "val_data_woe = t.transform(val[feature_list])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "id": "aeffa0c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>person_info</th>\n",
       "      <th>finance_info</th>\n",
       "      <th>credit_info</th>\n",
       "      <th>act_info</th>\n",
       "      <th>samp_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4275</th>\n",
       "      <td>-0.124495</td>\n",
       "      <td>-0.554842</td>\n",
       "      <td>-0.776175</td>\n",
       "      <td>0.497297</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23764</th>\n",
       "      <td>-0.324571</td>\n",
       "      <td>-0.554842</td>\n",
       "      <td>-0.776175</td>\n",
       "      <td>-0.276022</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33231</th>\n",
       "      <td>-0.124495</td>\n",
       "      <td>-0.554842</td>\n",
       "      <td>-0.161176</td>\n",
       "      <td>-0.276022</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57170</th>\n",
       "      <td>-0.926883</td>\n",
       "      <td>-0.554842</td>\n",
       "      <td>-0.776175</td>\n",
       "      <td>-0.439799</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56308</th>\n",
       "      <td>-1.377894</td>\n",
       "      <td>-0.554842</td>\n",
       "      <td>-0.776175</td>\n",
       "      <td>0.040321</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95801</th>\n",
       "      <td>0.615993</td>\n",
       "      <td>1.562403</td>\n",
       "      <td>0.967726</td>\n",
       "      <td>0.497297</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95802</th>\n",
       "      <td>0.615993</td>\n",
       "      <td>-0.554842</td>\n",
       "      <td>-0.776175</td>\n",
       "      <td>0.497297</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95803</th>\n",
       "      <td>0.615993</td>\n",
       "      <td>-0.554842</td>\n",
       "      <td>-0.776175</td>\n",
       "      <td>0.497297</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95804</th>\n",
       "      <td>0.615993</td>\n",
       "      <td>-0.554842</td>\n",
       "      <td>-0.161176</td>\n",
       "      <td>0.497297</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95805</th>\n",
       "      <td>0.615993</td>\n",
       "      <td>1.562403</td>\n",
       "      <td>0.967726</td>\n",
       "      <td>0.497297</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>94351 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       person_info  finance_info  credit_info  act_info  samp_type\n",
       "4275     -0.124495     -0.554842    -0.776175  0.497297          1\n",
       "23764    -0.324571     -0.554842    -0.776175 -0.276022          1\n",
       "33231    -0.124495     -0.554842    -0.161176 -0.276022          1\n",
       "57170    -0.926883     -0.554842    -0.776175 -0.439799          1\n",
       "56308    -1.377894     -0.554842    -0.776175  0.040321          1\n",
       "...            ...           ...          ...       ...        ...\n",
       "95801     0.615993      1.562403     0.967726  0.497297          3\n",
       "95802     0.615993     -0.554842    -0.776175  0.497297          3\n",
       "95803     0.615993     -0.554842    -0.776175  0.497297          3\n",
       "95804     0.615993     -0.554842    -0.161176  0.497297          3\n",
       "95805     0.615993      1.562403     0.967726  0.497297          3\n",
       "\n",
       "[94351 rows x 5 columns]"
      ]
     },
     "execution_count": 362,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data_woe['samp_type'] = 1\n",
    "test_data_woe['samp_type'] = 2\n",
    "val_data_woe['samp_type'] = 3\n",
    "woe_data = pd.concat([train_data_woe, test_data_woe, val_data_woe])\n",
    "woe_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "id": "ce83af3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\python\\software\\base\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: In a future version of pandas all arguments of Series.sort_values will be keyword-only\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>psi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>samp_type</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>act_info</td>\n",
       "      <td>0.000244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>person_info</td>\n",
       "      <td>0.000595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>finance_info</td>\n",
       "      <td>0.001321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>credit_info</td>\n",
       "      <td>0.001837</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        feature       psi\n",
       "0     samp_type  0.000000\n",
       "1      act_info  0.000244\n",
       "2   person_info  0.000595\n",
       "3  finance_info  0.001321\n",
       "4   credit_info  0.001837"
      ]
     },
     "execution_count": 363,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "psi_df = toad.metrics.PSI(train_data_woe, test_data_woe).sort_values(0) \n",
    "psi_df = psi_df.reset_index()\n",
    "psi_df = psi_df.rename(columns = {'index': 'feature', 0: 'psi'}) \n",
    "psi_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "id": "7bb38613",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['act_info', 'person_info', 'finance_info', 'credit_info']"
      ]
     },
     "execution_count": 364,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 取PSI小于0.1的特征\n",
    "feature_list = psi_df[psi_df['psi'] < 0.1][['feature']]\n",
    "feature_list = feature_list.values.reshape(1, len(feature_list))[0]\n",
    "feature_list = list(feature_list)\n",
    "\n",
    "feature_list.remove('samp_type')\n",
    "feature_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "89c13968",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "id": "2577e07f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=0.1, class_weight='balanced')"
      ]
     },
     "execution_count": 365,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LogisticRegression(C=0.1, class_weight='balanced') \n",
    "model.fit(train_data_woe[feature_list], train['bad_ind'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "id": "777de4ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_ks :  0.45216897040639187\n",
      "train_auc :  0.7953531127242122\n",
      "\n",
      "train_ks :  0.47689610131470594\n",
      "train_auc :  0.8008200179296525\n",
      "\n",
      "时间外_ks :  0.4293868379662898\n",
      "train_auc :  0.7675362845211503\n",
      "\n"
     ]
    }
   ],
   "source": [
    "x = train_data_woe[feature_list]\n",
    "y = train['bad_ind']\n",
    "y_pred = model.predict_proba(x)[:,1] \n",
    "fpr_dev, tpr_dev,_ = roc_curve(y, y_pred) \n",
    "train_ks = abs(fpr_dev - tpr_dev).max() \n",
    "print('train_ks : ', train_ks)\n",
    "print('train_auc : ',  roc_auc_score(y, y_pred))\n",
    "print()\n",
    "\n",
    "x = test_data_woe[feature_list]\n",
    "y = test_data['bad_ind']\n",
    "y_pred = model.predict_proba(x)[:,1] \n",
    "fpr_dev, tpr_dev,_ = roc_curve(y, y_pred) \n",
    "test_ks = abs(fpr_dev - tpr_dev).max() \n",
    "print('train_ks : ', test_ks)\n",
    "print('train_auc : ',  roc_auc_score(y, y_pred))\n",
    "print()\n",
    "\n",
    "x = val_data_woe[feature_list]\n",
    "y = val_data['bad_ind']\n",
    "y_pred = model.predict_proba(x)[:,1] \n",
    "fpr_dev, tpr_dev,_ = roc_curve(y, y_pred) \n",
    "val_ks = abs(fpr_dev - tpr_dev).max() \n",
    "print('时间外_ks : ', val_ks)\n",
    "print('train_auc : ',  roc_auc_score(y, y_pred))\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "id": "ed4e75fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 到目前为止，其实训练已经算还可以了，auc和ks值都已经达标\n",
    "# 但是我希望能让训练效果更好一些，进行了过采样（SMOTE）\n",
    "# 并将训练模型换成了lightgdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45d49eef",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9fa8badb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20835b85",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc80cc12",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "id": "ded259bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bad_ind\n",
       "0.0    61418\n",
       "1.0      991\n",
       "Name: obs_mth, dtype: int64"
      ]
     },
     "execution_count": 368,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# SMOTE过采样\n",
    "train_data.groupby(['bad_ind'])['obs_mth'].count()\n",
    "# 我们可以发现坏人的数据与好人的数据相差了50多倍\n",
    "# 即：坏账率大概在1.8%左右，，其实这是很正常的，因为一家公司的正常运营，如果坏账率高于5%，那么公司的运营就表示出现问题了\n",
    "# 由于坏人的数量只有1000多条，所以我们选择进行过采样，而不是欠采样"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "id": "694be8d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 去掉拟合不好的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "id": "0f19725a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def lgb_test(train_x,train_y,test_x,test_y): \n",
    "    import lightgbm as lgb \n",
    "    clf =lgb.LGBMClassifier(boosting_type = 'gbdt', \n",
    "                            objective = 'binary', \n",
    "                            metric = 'auc', \n",
    "                            learning_rate = 0.1,\n",
    "                            n_estimators = 24, \n",
    "                            max_depth = 4, \n",
    "                            num_leaves = 25, \n",
    "                            max_bin = 40, \n",
    "                            min_data_in_leaf = 5, \n",
    "                            bagging_fraction = 0.6, \n",
    "                            bagging_freq = 0, \n",
    "                            feature_fraction = 0.8)\n",
    "    \n",
    "    clf.fit(train_x,train_y,\n",
    "            eval_set=[(train_x,train_y), \n",
    "                      (test_x,test_y)],\n",
    "            eval_metric = 'auc')\n",
    "    \n",
    "    return clf,clf.best_score_['valid_1']['auc']    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "id": "79e688cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 需要特别注意的是：我们进行SMOTE处理的是进行过WOE变换后的数据，因为如果是变换前再做WOE，那么坏人的概率很可能趋向一个平衡而导致WOE失效\n",
    "train = pd.concat([train_data_woe, train_data['bad_ind']], axis=1)\n",
    "test = pd.concat([test_data_woe, test_data['bad_ind']], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "id": "9f497a15",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\ttraining's auc: 0.781248\tvalid_1's auc: 0.725561\n",
      "[2]\ttraining's auc: 0.787202\tvalid_1's auc: 0.705565\n",
      "[3]\ttraining's auc: 0.794847\tvalid_1's auc: 0.712097\n",
      "[4]\ttraining's auc: 0.794942\tvalid_1's auc: 0.714198\n",
      "[5]\ttraining's auc: 0.797943\tvalid_1's auc: 0.713479\n",
      "[6]\ttraining's auc: 0.799146\tvalid_1's auc: 0.711893\n",
      "[7]\ttraining's auc: 0.798645\tvalid_1's auc: 0.711592\n",
      "[8]\ttraining's auc: 0.79833\tvalid_1's auc: 0.711477\n",
      "[9]\ttraining's auc: 0.798363\tvalid_1's auc: 0.711458\n",
      "[10]\ttraining's auc: 0.798477\tvalid_1's auc: 0.711337\n",
      "[11]\ttraining's auc: 0.799137\tvalid_1's auc: 0.711458\n",
      "[12]\ttraining's auc: 0.799767\tvalid_1's auc: 0.711458\n",
      "[13]\ttraining's auc: 0.801176\tvalid_1's auc: 0.711614\n",
      "[14]\ttraining's auc: 0.801543\tvalid_1's auc: 0.708913\n",
      "[15]\ttraining's auc: 0.801728\tvalid_1's auc: 0.711614\n",
      "[16]\ttraining's auc: 0.801971\tvalid_1's auc: 0.711735\n",
      "[17]\ttraining's auc: 0.802921\tvalid_1's auc: 0.711614\n",
      "[18]\ttraining's auc: 0.802919\tvalid_1's auc: 0.711614\n",
      "[19]\ttraining's auc: 0.80299\tvalid_1's auc: 0.711614\n",
      "[20]\ttraining's auc: 0.803269\tvalid_1's auc: 0.708942\n",
      "[21]\ttraining's auc: 0.804183\tvalid_1's auc: 0.708113\n",
      "[22]\ttraining's auc: 0.80418\tvalid_1's auc: 0.708113\n",
      "[23]\ttraining's auc: 0.804323\tvalid_1's auc: 0.708113\n",
      "[24]\ttraining's auc: 0.804401\tvalid_1's auc: 0.708104\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>act_info</th>\n",
       "      <th>person_info</th>\n",
       "      <th>finance_info</th>\n",
       "      <th>credit_info</th>\n",
       "      <th>bad_ind</th>\n",
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       "      <th>0</th>\n",
       "      <td>59919</td>\n",
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       "      <td>0.615993</td>\n",
       "      <td>1.562403</td>\n",
       "      <td>0.705909</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51329</td>\n",
       "      <td>0.040321</td>\n",
       "      <td>0.615993</td>\n",
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       "      <th>2</th>\n",
       "      <td>66028</td>\n",
       "      <td>0.040321</td>\n",
       "      <td>0.615993</td>\n",
       "      <td>1.562403</td>\n",
       "      <td>0.705909</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.102843</td>\n",
       "      <td>0.000032</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>62052</td>\n",
       "      <td>0.040321</td>\n",
       "      <td>0.615993</td>\n",
       "      <td>1.562403</td>\n",
       "      <td>0.705909</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.102843</td>\n",
       "      <td>0.000048</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>56764</td>\n",
       "      <td>0.040321</td>\n",
       "      <td>0.615993</td>\n",
       "      <td>1.562403</td>\n",
       "      <td>0.705909</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.102843</td>\n",
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       "      <td>-1.377894</td>\n",
       "      <td>-0.554842</td>\n",
       "      <td>-0.776175</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002927</td>\n",
       "      <td>0.999920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62405</th>\n",
       "      <td>56345</td>\n",
       "      <td>-1.267362</td>\n",
       "      <td>-1.377894</td>\n",
       "      <td>-0.554842</td>\n",
       "      <td>-0.776175</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002927</td>\n",
       "      <td>0.999936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62406</th>\n",
       "      <td>40868</td>\n",
       "      <td>-1.267362</td>\n",
       "      <td>-1.377894</td>\n",
       "      <td>-0.554842</td>\n",
       "      <td>-0.776175</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002927</td>\n",
       "      <td>0.999952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62407</th>\n",
       "      <td>24422</td>\n",
       "      <td>-1.267362</td>\n",
       "      <td>-1.377894</td>\n",
       "      <td>-0.554842</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.002927</td>\n",
       "      <td>0.999968</td>\n",
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       "    <tr>\n",
       "      <th>62408</th>\n",
       "      <td>27130</td>\n",
       "      <td>-1.267362</td>\n",
       "      <td>-1.377894</td>\n",
       "      <td>-0.554842</td>\n",
       "      <td>-0.776175</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002927</td>\n",
       "      <td>0.999984</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>62409 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       index  act_info  person_info  finance_info  credit_info  bad_ind  \\\n",
       "0      59919  0.040321     0.615993      1.562403     0.705909      0.0   \n",
       "1      51329  0.040321     0.615993      1.562403     0.705909      0.0   \n",
       "2      66028  0.040321     0.615993      1.562403     0.705909      0.0   \n",
       "3      62052  0.040321     0.615993      1.562403     0.705909      0.0   \n",
       "4      56764  0.040321     0.615993      1.562403     0.705909      0.0   \n",
       "...      ...       ...          ...           ...          ...      ...   \n",
       "62404  45017 -1.267362    -1.377894     -0.554842    -0.776175      0.0   \n",
       "62405  56345 -1.267362    -1.377894     -0.554842    -0.776175      0.0   \n",
       "62406  40868 -1.267362    -1.377894     -0.554842    -0.776175      0.0   \n",
       "62407  24422 -1.267362    -1.377894     -0.554842    -0.776175      0.0   \n",
       "62408  27130 -1.267362    -1.377894     -0.554842    -0.776175      0.0   \n",
       "\n",
       "           pred      rank  \n",
       "0      0.102843  0.000000  \n",
       "1      0.102843  0.000016  \n",
       "2      0.102843  0.000032  \n",
       "3      0.102843  0.000048  \n",
       "4      0.102843  0.000064  \n",
       "...         ...       ...  \n",
       "62404  0.002927  0.999920  \n",
       "62405  0.002927  0.999936  \n",
       "62406  0.002927  0.999952  \n",
       "62407  0.002927  0.999968  \n",
       "62408  0.002927  0.999984  \n",
       "\n",
       "[62409 rows x 8 columns]"
      ]
     },
     "execution_count": 372,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_x = train[feature_list]\n",
    "train_y = train['bad_ind']\n",
    "test_x = val[feature_list]\n",
    "test_y = val['bad_ind']\n",
    "lgb_model,lgb_auc = lgb_test(train_x,train_y,test_x,test_y) \n",
    "sample = train_x.copy() \n",
    "sample['bad_ind'] = train_y\n",
    "sample['pred'] = lgb_model.predict_proba(train_x)[:,1] \n",
    "sample = sample.sort_values(by=['pred'],ascending=False).reset_index() \n",
    "sample['rank'] = np.array(sample.index)/len(sample)\n",
    "sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "id": "4d2f71d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(56578, 9)"
      ]
     },
     "execution_count": 373,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def weight(x, y):\n",
    "    if x == 0 and y < 0.1:\n",
    "        return 0.1\n",
    "    elif x == 1 and y > 0.7:\n",
    "        return 0.1\n",
    "    else:\n",
    "        return 1\n",
    "sample['weight'] = sample.apply(lambda x:weight(x.bad_ind,x['rank']),axis=1)\n",
    "smote_sample = sample[sample.weight == 1]\n",
    "drop_sample = sample[sample.weight < 1]\n",
    "smote_sample.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "id": "65fd4aa5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>act_info</th>\n",
       "      <th>person_info</th>\n",
       "      <th>finance_info</th>\n",
       "      <th>credit_info</th>\n",
       "      <th>bad_ind</th>\n",
       "      <th>pred</th>\n",
       "      <th>rank</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>56801</td>\n",
       "      <td>0.040321</td>\n",
       "      <td>0.615993</td>\n",
       "      <td>1.562403</td>\n",
       "      <td>0.705909</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.102843</td>\n",
       "      <td>0.000176</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>67021</td>\n",
       "      <td>0.040321</td>\n",
       "      <td>0.615993</td>\n",
       "      <td>1.562403</td>\n",
       "      <td>0.705909</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.102843</td>\n",
       "      <td>0.000224</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>55903</td>\n",
       "      <td>0.040321</td>\n",
       "      <td>0.615993</td>\n",
       "      <td>1.562403</td>\n",
       "      <td>0.705909</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.102843</td>\n",
       "      <td>0.000417</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>53469</td>\n",
       "      <td>0.040321</td>\n",
       "      <td>0.615993</td>\n",
       "      <td>1.562403</td>\n",
       "      <td>0.705909</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.102843</td>\n",
       "      <td>0.000513</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>63479</td>\n",
       "      <td>0.040321</td>\n",
       "      <td>0.615993</td>\n",
       "      <td>1.562403</td>\n",
       "      <td>0.705909</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.102843</td>\n",
       "      <td>0.000593</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62404</th>\n",
       "      <td>45017</td>\n",
       "      <td>-1.267362</td>\n",
       "      <td>-1.377894</td>\n",
       "      <td>-0.554842</td>\n",
       "      <td>-0.776175</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002927</td>\n",
       "      <td>0.999920</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62405</th>\n",
       "      <td>56345</td>\n",
       "      <td>-1.267362</td>\n",
       "      <td>-1.377894</td>\n",
       "      <td>-0.554842</td>\n",
       "      <td>-0.776175</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002927</td>\n",
       "      <td>0.999936</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62406</th>\n",
       "      <td>40868</td>\n",
       "      <td>-1.267362</td>\n",
       "      <td>-1.377894</td>\n",
       "      <td>-0.554842</td>\n",
       "      <td>-0.776175</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002927</td>\n",
       "      <td>0.999952</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62407</th>\n",
       "      <td>24422</td>\n",
       "      <td>-1.267362</td>\n",
       "      <td>-1.377894</td>\n",
       "      <td>-0.554842</td>\n",
       "      <td>-0.776175</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002927</td>\n",
       "      <td>0.999968</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62408</th>\n",
       "      <td>27130</td>\n",
       "      <td>-1.267362</td>\n",
       "      <td>-1.377894</td>\n",
       "      <td>-0.554842</td>\n",
       "      <td>-0.776175</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002927</td>\n",
       "      <td>0.999984</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>56578 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       index  act_info  person_info  finance_info  credit_info  bad_ind  \\\n",
       "11     56801  0.040321     0.615993      1.562403     0.705909      1.0   \n",
       "14     67021  0.040321     0.615993      1.562403     0.705909      1.0   \n",
       "26     55903  0.040321     0.615993      1.562403     0.705909      1.0   \n",
       "32     53469  0.040321     0.615993      1.562403     0.705909      1.0   \n",
       "37     63479  0.040321     0.615993      1.562403     0.705909      1.0   \n",
       "...      ...       ...          ...           ...          ...      ...   \n",
       "62404  45017 -1.267362    -1.377894     -0.554842    -0.776175      0.0   \n",
       "62405  56345 -1.267362    -1.377894     -0.554842    -0.776175      0.0   \n",
       "62406  40868 -1.267362    -1.377894     -0.554842    -0.776175      0.0   \n",
       "62407  24422 -1.267362    -1.377894     -0.554842    -0.776175      0.0   \n",
       "62408  27130 -1.267362    -1.377894     -0.554842    -0.776175      0.0   \n",
       "\n",
       "           pred      rank  weight  \n",
       "11     0.102843  0.000176     1.0  \n",
       "14     0.102843  0.000224     1.0  \n",
       "26     0.102843  0.000417     1.0  \n",
       "32     0.102843  0.000513     1.0  \n",
       "37     0.102843  0.000593     1.0  \n",
       "...         ...       ...     ...  \n",
       "62404  0.002927  0.999920     1.0  \n",
       "62405  0.002927  0.999936     1.0  \n",
       "62406  0.002927  0.999952     1.0  \n",
       "62407  0.002927  0.999968     1.0  \n",
       "62408  0.002927  0.999984     1.0  \n",
       "\n",
       "[56578 rows x 9 columns]"
      ]
     },
     "execution_count": 374,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smote_sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "id": "b352f386",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 重新调整一下feature_list\n",
    "feature_list = ['act_info', 'person_info', 'credit_info', 'finance_info']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "id": "2182cfd0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\python\\software\\base\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "smote_train = smote_sample[feature_list]\n",
    "smote_train['bad_ind'] = smote_sample[['bad_ind']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "id": "fc146351",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>act_info</th>\n",
       "      <th>person_info</th>\n",
       "      <th>credit_info</th>\n",
       "      <th>finance_info</th>\n",
       "      <th>bad_ind</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.040321</td>\n",
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       "      <td>1.562403</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.040321</td>\n",
       "      <td>0.615993</td>\n",
       "      <td>0.705909</td>\n",
       "      <td>1.562403</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.040321</td>\n",
       "      <td>0.615993</td>\n",
       "      <td>0.705909</td>\n",
       "      <td>1.562403</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.040321</td>\n",
       "      <td>0.615993</td>\n",
       "      <td>0.705909</td>\n",
       "      <td>1.562403</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.040321</td>\n",
       "      <td>0.615993</td>\n",
       "      <td>0.705909</td>\n",
       "      <td>1.562403</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62404</th>\n",
       "      <td>-1.267362</td>\n",
       "      <td>-1.377894</td>\n",
       "      <td>-0.776175</td>\n",
       "      <td>-0.554842</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62405</th>\n",
       "      <td>-1.267362</td>\n",
       "      <td>-1.377894</td>\n",
       "      <td>-0.776175</td>\n",
       "      <td>-0.554842</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62406</th>\n",
       "      <td>-1.267362</td>\n",
       "      <td>-1.377894</td>\n",
       "      <td>-0.776175</td>\n",
       "      <td>-0.554842</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62407</th>\n",
       "      <td>-1.267362</td>\n",
       "      <td>-1.377894</td>\n",
       "      <td>-0.776175</td>\n",
       "      <td>-0.554842</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62408</th>\n",
       "      <td>-1.267362</td>\n",
       "      <td>-1.377894</td>\n",
       "      <td>-0.776175</td>\n",
       "      <td>-0.554842</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>56578 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       act_info  person_info  credit_info  finance_info  bad_ind\n",
       "11     0.040321     0.615993     0.705909      1.562403      1.0\n",
       "14     0.040321     0.615993     0.705909      1.562403      1.0\n",
       "26     0.040321     0.615993     0.705909      1.562403      1.0\n",
       "32     0.040321     0.615993     0.705909      1.562403      1.0\n",
       "37     0.040321     0.615993     0.705909      1.562403      1.0\n",
       "...         ...          ...          ...           ...      ...\n",
       "62404 -1.267362    -1.377894    -0.776175     -0.554842      0.0\n",
       "62405 -1.267362    -1.377894    -0.776175     -0.554842      0.0\n",
       "62406 -1.267362    -1.377894    -0.776175     -0.554842      0.0\n",
       "62407 -1.267362    -1.377894    -0.776175     -0.554842      0.0\n",
       "62408 -1.267362    -1.377894    -0.776175     -0.554842      0.0\n",
       "\n",
       "[56578 rows x 5 columns]"
      ]
     },
     "execution_count": 377,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smote_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "id": "6dcebe70",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "badpctn: 0.5\n"
     ]
    }
   ],
   "source": [
    "def smote(train_x_smote,train_y_smote,K=15,random_state=0): \n",
    "    from imblearn.over_sampling import SMOTE \n",
    "    smote = SMOTE(k_neighbors=K, n_jobs=1,random_state=random_state) \n",
    "    rex,rey = smote.fit_resample(train_x_smote,train_y_smote) \n",
    "    return rex,rey\n",
    "\n",
    "rex,rey = smote(smote_train[feature_list], smote_train['bad_ind']) \n",
    "print('badpctn:',rey.sum()/len(rey))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "id": "e9f4bd4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 重新建模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "id": "f5b6b17a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=0.1, class_weight='balanced')"
      ]
     },
     "execution_count": 380,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LogisticRegression(C=0.1, class_weight='balanced') \n",
    "model.fit(smote_train[feature_list], smote_train['bad_ind'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "id": "7341d58f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_ks :  0.5587741939666181\n",
      "train_auc :  0.8752859401745351\n",
      "\n",
      "train_ks :  0.47485873299826786\n",
      "train_auc :  0.8016632592213987\n",
      "\n",
      "时间外_ks :  0.41937186587626085\n",
      "train_auc :  0.7650735082077607\n",
      "\n"
     ]
    }
   ],
   "source": [
    "x = smote_train[feature_list]\n",
    "y = smote_train['bad_ind']\n",
    "y_pred = model.predict_proba(x)[:,1] \n",
    "fpr_dev, tpr_dev,_ = roc_curve(y, y_pred) \n",
    "train_ks = abs(fpr_dev - tpr_dev).max() \n",
    "print('train_ks : ', train_ks)\n",
    "print('train_auc : ',  roc_auc_score(y, y_pred))\n",
    "print()\n",
    "\n",
    "x = test_data_woe[feature_list]\n",
    "y = test_data['bad_ind']\n",
    "y_pred = model.predict_proba(x)[:,1] \n",
    "fpr_dev, tpr_dev,_ = roc_curve(y, y_pred) \n",
    "test_ks = abs(fpr_dev - tpr_dev).max() \n",
    "print('train_ks : ', test_ks)\n",
    "print('train_auc : ',  roc_auc_score(y, y_pred))\n",
    "print()\n",
    "\n",
    "x = val_data_woe[feature_list]\n",
    "y = val_data['bad_ind']\n",
    "y_pred = model.predict_proba(x)[:,1] \n",
    "fpr_dev, tpr_dev,_ = roc_curve(y, y_pred) \n",
    "val_ks = abs(fpr_dev - tpr_dev).max() \n",
    "print('时间外_ks : ', val_ks)\n",
    "print('train_auc : ',  roc_auc_score(y, y_pred))\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "id": "2de8b9df",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 可以发现在训练集上提升显著，但测试集上和时间外样本几乎没有提升效果\n",
    "# 此时我们不妨考虑使用lightgbm，看看是否能对拟合效果还有提升"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "id": "84be0506",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x, train_y = smote_train[feature_list], smote_train['bad_ind']\n",
    "train_x = test_data_woe[feature_list]\n",
    "train_y = test_data['bad_ind']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "id": "8d98e996",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] Unknown parameter: max_features\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\python\\software\\base\\lib\\site-packages\\lightgbm\\sklearn.py:726: UserWarning: 'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. Pass 'early_stopping()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
      "E:\\python\\software\\base\\lib\\site-packages\\lightgbm\\engine.py:177: UserWarning: Found `num_iterations` in params. Will use it instead of argument\n",
      "  _log_warning(f\"Found `{alias}` in params. Will use it instead of argument\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "[2]\ttraining's auc: 0.71758\ttraining's binary_logloss: 0.0957721\tvalid_1's auc: 0.663958\tvalid_1's binary_logloss: 0.0993432\n",
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      "[6]\ttraining's auc: 0.751412\ttraining's binary_logloss: 0.0941857\tvalid_1's auc: 0.648006\tvalid_1's binary_logloss: 0.0983653\n",
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      "[8]\ttraining's auc: 0.75194\ttraining's binary_logloss: 0.0933968\tvalid_1's auc: 0.648006\tvalid_1's binary_logloss: 0.0979173\n",
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      "[21]\ttraining's auc: 0.785597\ttraining's binary_logloss: 0.08961\tvalid_1's auc: 0.683421\tvalid_1's binary_logloss: 0.0975151\n",
      "[22]\ttraining's auc: 0.789384\ttraining's binary_logloss: 0.0891723\tvalid_1's auc: 0.683606\tvalid_1's binary_logloss: 0.0975257\n",
      "[23]\ttraining's auc: 0.792538\ttraining's binary_logloss: 0.0888051\tvalid_1's auc: 0.6837\tvalid_1's binary_logloss: 0.0975933\n",
      "[24]\ttraining's auc: 0.794173\ttraining's binary_logloss: 0.0884611\tvalid_1's auc: 0.6837\tvalid_1's binary_logloss: 0.0976761\n",
      "[25]\ttraining's auc: 0.793845\ttraining's binary_logloss: 0.0883195\tvalid_1's auc: 0.6837\tvalid_1's binary_logloss: 0.0976496\n",
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      "[119]\ttraining's auc: 0.806544\ttraining's binary_logloss: 0.0839995\tvalid_1's auc: 0.710053\tvalid_1's binary_logloss: 0.104624\n",
      "[120]\ttraining's auc: 0.806424\ttraining's binary_logloss: 0.083997\tvalid_1's auc: 0.710053\tvalid_1's binary_logloss: 0.104662\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LGBMClassifier(colsample_bytree=0.7, learning_rate=0.05, max_depth=2,\n",
       "               max_features=140, min_child_weight=50, n_estimators=800,\n",
       "               n_jobs=7, num_iterations=800, objective='binary', reg_lambda=1,\n",
       "               subsample=0.7, subsample_freq=1)"
      ]
     },
     "execution_count": 384,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#定义lgb函数 \n",
    "import lightgbm as lgb\n",
    "from multiprocessing import cpu_count\n",
    "lt_model = lgb.LGBMClassifier(\n",
    "        boosting_type='gbdt', num_leaves=31, reg_alpha=0.0, reg_lambda=1,\n",
    "        max_depth=2, n_estimators=800,max_features = 140, objective='binary', \n",
    "        subsample=0.7, colsample_bytree=0.7, subsample_freq=1, \n",
    "        learning_rate=0.05, min_child_weight=50,random_state=None,n_jobs=cpu_count()-1, \n",
    "        num_iterations = 800 #迭代次数 \n",
    "    )\n",
    "lt_model.fit(train_x, train_y,eval_set=[(train_x, train_y), (test_x,test_y)],eval_metric='auc',early_stopping_rounds=100) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "id": "4df31862",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_ks :  0.5020078659318135\n",
      "train_auc :  0.8337787562703881\n",
      "\n",
      "train_ks :  0.45034216429565266\n",
      "train_auc :  0.7844781740961144\n",
      "\n",
      "时间外_ks :  0.4179640919244241\n",
      "train_auc :  0.751685626637694\n",
      "\n"
     ]
    }
   ],
   "source": [
    "x = smote_train[feature_list]\n",
    "y = smote_train['bad_ind']\n",
    "y_pred = lt_model.predict_proba(x)[:,1] \n",
    "fpr_dev, tpr_dev,_ = roc_curve(y, y_pred) \n",
    "train_ks = abs(fpr_dev - tpr_dev).max() \n",
    "print('train_ks : ', train_ks)\n",
    "print('train_auc : ',  roc_auc_score(y, y_pred))\n",
    "print()\n",
    "\n",
    "x = test_data_woe[feature_list]\n",
    "y = test_data['bad_ind']\n",
    "y_pred = lt_model.predict_proba(x)[:,1] \n",
    "fpr_dev, tpr_dev,_ = roc_curve(y, y_pred) \n",
    "test_ks = abs(fpr_dev - tpr_dev).max() \n",
    "print('train_ks : ', test_ks)\n",
    "print('train_auc : ',  roc_auc_score(y, y_pred))\n",
    "print()\n",
    "\n",
    "x = val_data_woe[feature_list]\n",
    "y = val_data['bad_ind']\n",
    "y_pred = lt_model.predict_proba(x)[:,1] \n",
    "fpr_dev, tpr_dev,_ = roc_curve(y, y_pred) \n",
    "val_ks = abs(fpr_dev - tpr_dev).max() \n",
    "print('时间外_ks : ', val_ks)\n",
    "print('train_auc : ',  roc_auc_score(y, y_pred))\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "id": "89da1c29",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 可以发现在这个案例里lightgbm并没有比逻辑回归更好用，SMOTE过采样后模型效果也没有得到显著的提升\n",
    "# 因此可以直接使用WOE+逻辑回归 来处理该案例\n",
    "# 事实上还可以不做WOE变换，然后对数据进行SMOTE处理，然后再对比看看，但目前为止看起来该模型还不错，就不再做过多的处置了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "235b1627",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "id": "88fdf649",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['act_info', 'person_info', 'credit_info', 'finance_info']"
      ]
     },
     "execution_count": 387,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "id": "99951de1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 生成评分卡\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe788559",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "id": "4d809f4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>value</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>act_info</td>\n",
       "      <td>[-inf ~ 0.1153846153846154)</td>\n",
       "      <td>68.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>act_info</td>\n",
       "      <td>[0.1153846153846154 ~ 0.2051282051282051)</td>\n",
       "      <td>89.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>act_info</td>\n",
       "      <td>[0.2051282051282051 ~ 0.3205128205128205)</td>\n",
       "      <td>104.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>act_info</td>\n",
       "      <td>[0.3205128205128205 ~ 0.5256410256410257)</td>\n",
       "      <td>112.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>act_info</td>\n",
       "      <td>[0.5256410256410257 ~ inf)</td>\n",
       "      <td>150.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>person_info</td>\n",
       "      <td>[-inf ~ -0.2610139784946237)</td>\n",
       "      <td>169.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>person_info</td>\n",
       "      <td>[-0.2610139784946237 ~ -0.1286774193548387)</td>\n",
       "      <td>144.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>person_info</td>\n",
       "      <td>[-0.1286774193548387 ~ -0.0537175627240143)</td>\n",
       "      <td>110.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>person_info</td>\n",
       "      <td>[-0.0537175627240143 ~ 0.0626602150537634)</td>\n",
       "      <td>98.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>person_info</td>\n",
       "      <td>[0.0626602150537634 ~ 0.078853046594982)</td>\n",
       "      <td>79.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>person_info</td>\n",
       "      <td>[0.078853046594982 ~ inf)</td>\n",
       "      <td>56.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>credit_info</td>\n",
       "      <td>[-inf ~ 0.02)</td>\n",
       "      <td>127.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>credit_info</td>\n",
       "      <td>[0.02 ~ 0.04)</td>\n",
       "      <td>99.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>credit_info</td>\n",
       "      <td>[0.04 ~ 0.09)</td>\n",
       "      <td>65.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>credit_info</td>\n",
       "      <td>[0.09 ~ 0.14)</td>\n",
       "      <td>59.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>credit_info</td>\n",
       "      <td>[0.14 ~ inf)</td>\n",
       "      <td>47.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>finance_info</td>\n",
       "      <td>[-inf ~ 0.0476190476190476)</td>\n",
       "      <td>121.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>finance_info</td>\n",
       "      <td>[0.0476190476190476 ~ 0.0714285714285714)</td>\n",
       "      <td>65.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>finance_info</td>\n",
       "      <td>[0.0714285714285714 ~ inf)</td>\n",
       "      <td>8.72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            name                                        value   score\n",
       "0       act_info                  [-inf ~ 0.1153846153846154)   68.85\n",
       "1       act_info    [0.1153846153846154 ~ 0.2051282051282051)   89.89\n",
       "2       act_info    [0.2051282051282051 ~ 0.3205128205128205)  104.46\n",
       "3       act_info    [0.3205128205128205 ~ 0.5256410256410257)  112.01\n",
       "4       act_info                   [0.5256410256410257 ~ inf)  150.12\n",
       "5    person_info                 [-inf ~ -0.2610139784946237)  169.61\n",
       "6    person_info  [-0.2610139784946237 ~ -0.1286774193548387)  144.12\n",
       "7    person_info  [-0.1286774193548387 ~ -0.0537175627240143)  110.09\n",
       "8    person_info   [-0.0537175627240143 ~ 0.0626602150537634)   98.78\n",
       "9    person_info     [0.0626602150537634 ~ 0.078853046594982)   79.44\n",
       "10   person_info                    [0.078853046594982 ~ inf)   56.94\n",
       "11   credit_info                                [-inf ~ 0.02)  127.36\n",
       "12   credit_info                                [0.02 ~ 0.04)   99.14\n",
       "13   credit_info                                [0.04 ~ 0.09)   65.87\n",
       "14   credit_info                                [0.09 ~ 0.14)   59.36\n",
       "15   credit_info                                 [0.14 ~ inf)   47.35\n",
       "16  finance_info                  [-inf ~ 0.0476190476190476)  121.24\n",
       "17  finance_info    [0.0476190476190476 ~ 0.0714285714285714)   65.92\n",
       "18  finance_info                   [0.0714285714285714 ~ inf)    8.72"
      ]
     },
     "execution_count": 398,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from toad.scorecard import ScoreCard \n",
    "card = ScoreCard(combiner=combiner, \n",
    "                 transer=t, \n",
    "                 C=0.1, \n",
    "                 class_weight='balanced', \n",
    "                 base_score=650, \n",
    "                 base_odds=50, \n",
    "                 pdo=50, \n",
    "                 rate=2) \n",
    "card.fit(train_data_woe[feature_list], train_data['bad_ind']) \n",
    "final_card = card.export(to_frame=True) \n",
    "final_card"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "99d52ee7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "id": "3fa193fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>bads</th>\n",
       "      <th>goods</th>\n",
       "      <th>total</th>\n",
       "      <th>bad_rate</th>\n",
       "      <th>good_rate</th>\n",
       "      <th>odds</th>\n",
       "      <th>bad_prop</th>\n",
       "      <th>good_prop</th>\n",
       "      <th>...</th>\n",
       "      <th>cum_bad_rate_rev</th>\n",
       "      <th>cum_bads_prop</th>\n",
       "      <th>cum_bads_prop_rev</th>\n",
       "      <th>cum_goods_prop</th>\n",
       "      <th>cum_goods_prop_rev</th>\n",
       "      <th>cum_total_prop</th>\n",
       "      <th>cum_total_prop_rev</th>\n",
       "      <th>ks</th>\n",
       "      <th>lift</th>\n",
       "      <th>cum_lift</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.011201</td>\n",
       "      <td>0.052804</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1023.0</td>\n",
       "      <td>1025.0</td>\n",
       "      <td>0.001951</td>\n",
       "      <td>0.998049</td>\n",
       "      <td>0.001955</td>\n",
       "      <td>0.006098</td>\n",
       "      <td>0.065380</td>\n",
       "      <td>...</td>\n",
       "      <td>0.020532</td>\n",
       "      <td>0.006098</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.065380</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.064163</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059282</td>\n",
       "      <td>0.095033</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.053540</td>\n",
       "      <td>0.095121</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1014.0</td>\n",
       "      <td>1017.0</td>\n",
       "      <td>0.002950</td>\n",
       "      <td>0.997050</td>\n",
       "      <td>0.002959</td>\n",
       "      <td>0.009146</td>\n",
       "      <td>0.064805</td>\n",
       "      <td>...</td>\n",
       "      <td>0.021806</td>\n",
       "      <td>0.015244</td>\n",
       "      <td>0.993902</td>\n",
       "      <td>0.130185</td>\n",
       "      <td>0.934620</td>\n",
       "      <td>0.127825</td>\n",
       "      <td>0.935837</td>\n",
       "      <td>0.114941</td>\n",
       "      <td>0.143670</td>\n",
       "      <td>1.062046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.095315</td>\n",
       "      <td>0.127614</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>1141.0</td>\n",
       "      <td>0.002629</td>\n",
       "      <td>0.997371</td>\n",
       "      <td>0.002636</td>\n",
       "      <td>0.009146</td>\n",
       "      <td>0.072730</td>\n",
       "      <td>...</td>\n",
       "      <td>0.023182</td>\n",
       "      <td>0.024390</td>\n",
       "      <td>0.984756</td>\n",
       "      <td>0.202914</td>\n",
       "      <td>0.869815</td>\n",
       "      <td>0.199249</td>\n",
       "      <td>0.872175</td>\n",
       "      <td>0.178524</td>\n",
       "      <td>0.128057</td>\n",
       "      <td>1.129081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.130169</td>\n",
       "      <td>0.200913</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1060.0</td>\n",
       "      <td>1064.0</td>\n",
       "      <td>0.003759</td>\n",
       "      <td>0.996241</td>\n",
       "      <td>0.003774</td>\n",
       "      <td>0.012195</td>\n",
       "      <td>0.067745</td>\n",
       "      <td>...</td>\n",
       "      <td>0.025016</td>\n",
       "      <td>0.036585</td>\n",
       "      <td>0.975610</td>\n",
       "      <td>0.270659</td>\n",
       "      <td>0.797086</td>\n",
       "      <td>0.265853</td>\n",
       "      <td>0.800751</td>\n",
       "      <td>0.234074</td>\n",
       "      <td>0.183099</td>\n",
       "      <td>1.218368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.201967</td>\n",
       "      <td>0.260453</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1066.0</td>\n",
       "      <td>1073.0</td>\n",
       "      <td>0.006524</td>\n",
       "      <td>0.993476</td>\n",
       "      <td>0.006567</td>\n",
       "      <td>0.021341</td>\n",
       "      <td>0.068128</td>\n",
       "      <td>...</td>\n",
       "      <td>0.026944</td>\n",
       "      <td>0.057927</td>\n",
       "      <td>0.963415</td>\n",
       "      <td>0.338787</td>\n",
       "      <td>0.729341</td>\n",
       "      <td>0.333020</td>\n",
       "      <td>0.734147</td>\n",
       "      <td>0.280860</td>\n",
       "      <td>0.317735</td>\n",
       "      <td>1.312291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.266311</td>\n",
       "      <td>0.349440</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1037.0</td>\n",
       "      <td>1045.0</td>\n",
       "      <td>0.007656</td>\n",
       "      <td>0.992344</td>\n",
       "      <td>0.007715</td>\n",
       "      <td>0.024390</td>\n",
       "      <td>0.066275</td>\n",
       "      <td>...</td>\n",
       "      <td>0.029000</td>\n",
       "      <td>0.082317</td>\n",
       "      <td>0.942073</td>\n",
       "      <td>0.405062</td>\n",
       "      <td>0.661213</td>\n",
       "      <td>0.398435</td>\n",
       "      <td>0.666980</td>\n",
       "      <td>0.322745</td>\n",
       "      <td>0.372856</td>\n",
       "      <td>1.412447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.349499</td>\n",
       "      <td>0.430947</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1067.0</td>\n",
       "      <td>1080.0</td>\n",
       "      <td>0.012037</td>\n",
       "      <td>0.987963</td>\n",
       "      <td>0.012184</td>\n",
       "      <td>0.039634</td>\n",
       "      <td>0.068192</td>\n",
       "      <td>...</td>\n",
       "      <td>0.031322</td>\n",
       "      <td>0.121951</td>\n",
       "      <td>0.917683</td>\n",
       "      <td>0.473254</td>\n",
       "      <td>0.594938</td>\n",
       "      <td>0.466041</td>\n",
       "      <td>0.601565</td>\n",
       "      <td>0.351302</td>\n",
       "      <td>0.586255</td>\n",
       "      <td>1.525493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.441402</td>\n",
       "      <td>0.487820</td>\n",
       "      <td>4.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>364.0</td>\n",
       "      <td>0.010989</td>\n",
       "      <td>0.989011</td>\n",
       "      <td>0.011111</td>\n",
       "      <td>0.012195</td>\n",
       "      <td>0.023008</td>\n",
       "      <td>...</td>\n",
       "      <td>0.033763</td>\n",
       "      <td>0.134146</td>\n",
       "      <td>0.878049</td>\n",
       "      <td>0.496261</td>\n",
       "      <td>0.526746</td>\n",
       "      <td>0.488826</td>\n",
       "      <td>0.533959</td>\n",
       "      <td>0.362115</td>\n",
       "      <td>0.535212</td>\n",
       "      <td>1.644411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.487820</td>\n",
       "      <td>0.525868</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1747.0</td>\n",
       "      <td>1770.0</td>\n",
       "      <td>0.012994</td>\n",
       "      <td>0.987006</td>\n",
       "      <td>0.013165</td>\n",
       "      <td>0.070122</td>\n",
       "      <td>0.111651</td>\n",
       "      <td>...</td>\n",
       "      <td>0.034778</td>\n",
       "      <td>0.204268</td>\n",
       "      <td>0.865854</td>\n",
       "      <td>0.607912</td>\n",
       "      <td>0.503739</td>\n",
       "      <td>0.599624</td>\n",
       "      <td>0.511174</td>\n",
       "      <td>0.403644</td>\n",
       "      <td>0.632880</td>\n",
       "      <td>1.693854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.527368</td>\n",
       "      <td>0.642341</td>\n",
       "      <td>13.0</td>\n",
       "      <td>647.0</td>\n",
       "      <td>660.0</td>\n",
       "      <td>0.019697</td>\n",
       "      <td>0.980303</td>\n",
       "      <td>0.020093</td>\n",
       "      <td>0.039634</td>\n",
       "      <td>0.041350</td>\n",
       "      <td>...</td>\n",
       "      <td>0.040807</td>\n",
       "      <td>0.243902</td>\n",
       "      <td>0.795732</td>\n",
       "      <td>0.649262</td>\n",
       "      <td>0.392088</td>\n",
       "      <td>0.640939</td>\n",
       "      <td>0.400376</td>\n",
       "      <td>0.405359</td>\n",
       "      <td>0.959326</td>\n",
       "      <td>1.987463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.642341</td>\n",
       "      <td>0.789021</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1301.0</td>\n",
       "      <td>1327.0</td>\n",
       "      <td>0.019593</td>\n",
       "      <td>0.980407</td>\n",
       "      <td>0.019985</td>\n",
       "      <td>0.079268</td>\n",
       "      <td>0.083147</td>\n",
       "      <td>...</td>\n",
       "      <td>0.043236</td>\n",
       "      <td>0.323171</td>\n",
       "      <td>0.756098</td>\n",
       "      <td>0.732409</td>\n",
       "      <td>0.350738</td>\n",
       "      <td>0.724006</td>\n",
       "      <td>0.359061</td>\n",
       "      <td>0.409238</td>\n",
       "      <td>0.954266</td>\n",
       "      <td>2.105763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.791433</td>\n",
       "      <td>0.851521</td>\n",
       "      <td>25.0</td>\n",
       "      <td>876.0</td>\n",
       "      <td>901.0</td>\n",
       "      <td>0.027747</td>\n",
       "      <td>0.972253</td>\n",
       "      <td>0.028539</td>\n",
       "      <td>0.076220</td>\n",
       "      <td>0.055985</td>\n",
       "      <td>...</td>\n",
       "      <td>0.050352</td>\n",
       "      <td>0.399390</td>\n",
       "      <td>0.676829</td>\n",
       "      <td>0.788394</td>\n",
       "      <td>0.267591</td>\n",
       "      <td>0.780407</td>\n",
       "      <td>0.275994</td>\n",
       "      <td>0.389004</td>\n",
       "      <td>1.351395</td>\n",
       "      <td>2.452336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.851928</td>\n",
       "      <td>0.946995</td>\n",
       "      <td>61.0</td>\n",
       "      <td>1245.0</td>\n",
       "      <td>1306.0</td>\n",
       "      <td>0.046708</td>\n",
       "      <td>0.953292</td>\n",
       "      <td>0.048996</td>\n",
       "      <td>0.185976</td>\n",
       "      <td>0.079568</td>\n",
       "      <td>...</td>\n",
       "      <td>0.056157</td>\n",
       "      <td>0.585366</td>\n",
       "      <td>0.600610</td>\n",
       "      <td>0.867962</td>\n",
       "      <td>0.211606</td>\n",
       "      <td>0.862160</td>\n",
       "      <td>0.219593</td>\n",
       "      <td>0.282596</td>\n",
       "      <td>2.274855</td>\n",
       "      <td>2.735103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.949171</td>\n",
       "      <td>0.983110</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1058.0</td>\n",
       "      <td>1118.0</td>\n",
       "      <td>0.053667</td>\n",
       "      <td>0.946333</td>\n",
       "      <td>0.056711</td>\n",
       "      <td>0.182927</td>\n",
       "      <td>0.067617</td>\n",
       "      <td>...</td>\n",
       "      <td>0.061762</td>\n",
       "      <td>0.768293</td>\n",
       "      <td>0.414634</td>\n",
       "      <td>0.935579</td>\n",
       "      <td>0.132038</td>\n",
       "      <td>0.932144</td>\n",
       "      <td>0.137840</td>\n",
       "      <td>0.167286</td>\n",
       "      <td>2.613825</td>\n",
       "      <td>3.008075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.983531</td>\n",
       "      <td>0.993247</td>\n",
       "      <td>76.0</td>\n",
       "      <td>1008.0</td>\n",
       "      <td>1084.0</td>\n",
       "      <td>0.070111</td>\n",
       "      <td>0.929889</td>\n",
       "      <td>0.075397</td>\n",
       "      <td>0.231707</td>\n",
       "      <td>0.064421</td>\n",
       "      <td>...</td>\n",
       "      <td>0.070111</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.231707</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.064421</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.067856</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>3.414690</td>\n",
       "      <td>3.414690</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         min       max  bads   goods   total  bad_rate  good_rate      odds  \\\n",
       "0   0.011201  0.052804   2.0  1023.0  1025.0  0.001951   0.998049  0.001955   \n",
       "1   0.053540  0.095121   3.0  1014.0  1017.0  0.002950   0.997050  0.002959   \n",
       "2   0.095315  0.127614   3.0  1138.0  1141.0  0.002629   0.997371  0.002636   \n",
       "3   0.130169  0.200913   4.0  1060.0  1064.0  0.003759   0.996241  0.003774   \n",
       "4   0.201967  0.260453   7.0  1066.0  1073.0  0.006524   0.993476  0.006567   \n",
       "5   0.266311  0.349440   8.0  1037.0  1045.0  0.007656   0.992344  0.007715   \n",
       "6   0.349499  0.430947  13.0  1067.0  1080.0  0.012037   0.987963  0.012184   \n",
       "7   0.441402  0.487820   4.0   360.0   364.0  0.010989   0.989011  0.011111   \n",
       "8   0.487820  0.525868  23.0  1747.0  1770.0  0.012994   0.987006  0.013165   \n",
       "9   0.527368  0.642341  13.0   647.0   660.0  0.019697   0.980303  0.020093   \n",
       "10  0.642341  0.789021  26.0  1301.0  1327.0  0.019593   0.980407  0.019985   \n",
       "11  0.791433  0.851521  25.0   876.0   901.0  0.027747   0.972253  0.028539   \n",
       "12  0.851928  0.946995  61.0  1245.0  1306.0  0.046708   0.953292  0.048996   \n",
       "13  0.949171  0.983110  60.0  1058.0  1118.0  0.053667   0.946333  0.056711   \n",
       "14  0.983531  0.993247  76.0  1008.0  1084.0  0.070111   0.929889  0.075397   \n",
       "\n",
       "    bad_prop  good_prop  ...  cum_bad_rate_rev  cum_bads_prop  \\\n",
       "0   0.006098   0.065380  ...          0.020532       0.006098   \n",
       "1   0.009146   0.064805  ...          0.021806       0.015244   \n",
       "2   0.009146   0.072730  ...          0.023182       0.024390   \n",
       "3   0.012195   0.067745  ...          0.025016       0.036585   \n",
       "4   0.021341   0.068128  ...          0.026944       0.057927   \n",
       "5   0.024390   0.066275  ...          0.029000       0.082317   \n",
       "6   0.039634   0.068192  ...          0.031322       0.121951   \n",
       "7   0.012195   0.023008  ...          0.033763       0.134146   \n",
       "8   0.070122   0.111651  ...          0.034778       0.204268   \n",
       "9   0.039634   0.041350  ...          0.040807       0.243902   \n",
       "10  0.079268   0.083147  ...          0.043236       0.323171   \n",
       "11  0.076220   0.055985  ...          0.050352       0.399390   \n",
       "12  0.185976   0.079568  ...          0.056157       0.585366   \n",
       "13  0.182927   0.067617  ...          0.061762       0.768293   \n",
       "14  0.231707   0.064421  ...          0.070111       1.000000   \n",
       "\n",
       "    cum_bads_prop_rev  cum_goods_prop  cum_goods_prop_rev  cum_total_prop  \\\n",
       "0            1.000000        0.065380            1.000000        0.064163   \n",
       "1            0.993902        0.130185            0.934620        0.127825   \n",
       "2            0.984756        0.202914            0.869815        0.199249   \n",
       "3            0.975610        0.270659            0.797086        0.265853   \n",
       "4            0.963415        0.338787            0.729341        0.333020   \n",
       "5            0.942073        0.405062            0.661213        0.398435   \n",
       "6            0.917683        0.473254            0.594938        0.466041   \n",
       "7            0.878049        0.496261            0.526746        0.488826   \n",
       "8            0.865854        0.607912            0.503739        0.599624   \n",
       "9            0.795732        0.649262            0.392088        0.640939   \n",
       "10           0.756098        0.732409            0.350738        0.724006   \n",
       "11           0.676829        0.788394            0.267591        0.780407   \n",
       "12           0.600610        0.867962            0.211606        0.862160   \n",
       "13           0.414634        0.935579            0.132038        0.932144   \n",
       "14           0.231707        1.000000            0.064421        1.000000   \n",
       "\n",
       "    cum_total_prop_rev        ks      lift  cum_lift  \n",
       "0             1.000000  0.059282  0.095033  1.000000  \n",
       "1             0.935837  0.114941  0.143670  1.062046  \n",
       "2             0.872175  0.178524  0.128057  1.129081  \n",
       "3             0.800751  0.234074  0.183099  1.218368  \n",
       "4             0.734147  0.280860  0.317735  1.312291  \n",
       "5             0.666980  0.322745  0.372856  1.412447  \n",
       "6             0.601565  0.351302  0.586255  1.525493  \n",
       "7             0.533959  0.362115  0.535212  1.644411  \n",
       "8             0.511174  0.403644  0.632880  1.693854  \n",
       "9             0.400376  0.405359  0.959326  1.987463  \n",
       "10            0.359061  0.409238  0.954266  2.105763  \n",
       "11            0.275994  0.389004  1.351395  2.452336  \n",
       "12            0.219593  0.282596  2.274855  2.735103  \n",
       "13            0.137840  0.167286  2.613825  3.008075  \n",
       "14            0.067856 -0.000000  3.414690  3.414690  \n",
       "\n",
       "[15 rows x 22 columns]"
      ]
     },
     "execution_count": 400,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 生成时间外报告\n",
    "x = val_data_woe[feature_list]\n",
    "y = val_data['bad_ind']\n",
    "y_pred = model.predict_proba(x)[:,1] \n",
    "toad.metrics.KS_bucket(y_pred, y, bucket=5, method='quantile')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "id": "0548efd0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>bads</th>\n",
       "      <th>goods</th>\n",
       "      <th>total</th>\n",
       "      <th>bad_rate</th>\n",
       "      <th>good_rate</th>\n",
       "      <th>odds</th>\n",
       "      <th>bad_prop</th>\n",
       "      <th>good_prop</th>\n",
       "      <th>...</th>\n",
       "      <th>cum_bad_rate_rev</th>\n",
       "      <th>cum_bads_prop</th>\n",
       "      <th>cum_bads_prop_rev</th>\n",
       "      <th>cum_goods_prop</th>\n",
       "      <th>cum_goods_prop_rev</th>\n",
       "      <th>cum_total_prop</th>\n",
       "      <th>cum_total_prop_rev</th>\n",
       "      <th>ks</th>\n",
       "      <th>lift</th>\n",
       "      <th>cum_lift</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.095228</td>\n",
       "      <td>0.095228</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1172.0</td>\n",
       "      <td>1177.0</td>\n",
       "      <td>0.004248</td>\n",
       "      <td>0.995752</td>\n",
       "      <td>0.004266</td>\n",
       "      <td>0.015244</td>\n",
       "      <td>0.074903</td>\n",
       "      <td>...</td>\n",
       "      <td>0.020532</td>\n",
       "      <td>0.015244</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.074903</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.073678</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059659</td>\n",
       "      <td>0.206900</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.160692</td>\n",
       "      <td>0.241909</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2866.0</td>\n",
       "      <td>2878.0</td>\n",
       "      <td>0.004170</td>\n",
       "      <td>0.995830</td>\n",
       "      <td>0.004187</td>\n",
       "      <td>0.036585</td>\n",
       "      <td>0.183166</td>\n",
       "      <td>...</td>\n",
       "      <td>0.021827</td>\n",
       "      <td>0.051829</td>\n",
       "      <td>0.984756</td>\n",
       "      <td>0.258069</td>\n",
       "      <td>0.925097</td>\n",
       "      <td>0.253834</td>\n",
       "      <td>0.926322</td>\n",
       "      <td>0.206239</td>\n",
       "      <td>0.203075</td>\n",
       "      <td>1.063081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.304076</td>\n",
       "      <td>0.333679</td>\n",
       "      <td>4.0</td>\n",
       "      <td>926.0</td>\n",
       "      <td>930.0</td>\n",
       "      <td>0.004301</td>\n",
       "      <td>0.995699</td>\n",
       "      <td>0.004320</td>\n",
       "      <td>0.012195</td>\n",
       "      <td>0.059181</td>\n",
       "      <td>...</td>\n",
       "      <td>0.026091</td>\n",
       "      <td>0.064024</td>\n",
       "      <td>0.948171</td>\n",
       "      <td>0.317249</td>\n",
       "      <td>0.741931</td>\n",
       "      <td>0.312050</td>\n",
       "      <td>0.746166</td>\n",
       "      <td>0.253225</td>\n",
       "      <td>0.209481</td>\n",
       "      <td>1.270724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.334144</td>\n",
       "      <td>0.421377</td>\n",
       "      <td>44.0</td>\n",
       "      <td>2825.0</td>\n",
       "      <td>2869.0</td>\n",
       "      <td>0.015336</td>\n",
       "      <td>0.984664</td>\n",
       "      <td>0.015575</td>\n",
       "      <td>0.134146</td>\n",
       "      <td>0.180546</td>\n",
       "      <td>...</td>\n",
       "      <td>0.027934</td>\n",
       "      <td>0.198171</td>\n",
       "      <td>0.935976</td>\n",
       "      <td>0.497795</td>\n",
       "      <td>0.682751</td>\n",
       "      <td>0.491643</td>\n",
       "      <td>0.687950</td>\n",
       "      <td>0.299624</td>\n",
       "      <td>0.746946</td>\n",
       "      <td>1.360529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.476701</td>\n",
       "      <td>0.476701</td>\n",
       "      <td>1.0</td>\n",
       "      <td>484.0</td>\n",
       "      <td>485.0</td>\n",
       "      <td>0.002062</td>\n",
       "      <td>0.997938</td>\n",
       "      <td>0.002066</td>\n",
       "      <td>0.003049</td>\n",
       "      <td>0.030932</td>\n",
       "      <td>...</td>\n",
       "      <td>0.032385</td>\n",
       "      <td>0.201220</td>\n",
       "      <td>0.801829</td>\n",
       "      <td>0.528728</td>\n",
       "      <td>0.502205</td>\n",
       "      <td>0.522003</td>\n",
       "      <td>0.508357</td>\n",
       "      <td>0.327508</td>\n",
       "      <td>0.100421</td>\n",
       "      <td>1.577296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.477222</td>\n",
       "      <td>0.602904</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2122.0</td>\n",
       "      <td>2144.0</td>\n",
       "      <td>0.010261</td>\n",
       "      <td>0.989739</td>\n",
       "      <td>0.010368</td>\n",
       "      <td>0.067073</td>\n",
       "      <td>0.135617</td>\n",
       "      <td>...</td>\n",
       "      <td>0.034311</td>\n",
       "      <td>0.268293</td>\n",
       "      <td>0.798780</td>\n",
       "      <td>0.664345</td>\n",
       "      <td>0.471272</td>\n",
       "      <td>0.656213</td>\n",
       "      <td>0.477997</td>\n",
       "      <td>0.396052</td>\n",
       "      <td>0.499764</td>\n",
       "      <td>1.671100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.603404</td>\n",
       "      <td>0.734175</td>\n",
       "      <td>76.0</td>\n",
       "      <td>2138.0</td>\n",
       "      <td>2214.0</td>\n",
       "      <td>0.034327</td>\n",
       "      <td>0.965673</td>\n",
       "      <td>0.035547</td>\n",
       "      <td>0.231707</td>\n",
       "      <td>0.136640</td>\n",
       "      <td>...</td>\n",
       "      <td>0.043700</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.731707</td>\n",
       "      <td>0.800984</td>\n",
       "      <td>0.335655</td>\n",
       "      <td>0.794804</td>\n",
       "      <td>0.343787</td>\n",
       "      <td>0.300984</td>\n",
       "      <td>1.671872</td>\n",
       "      <td>2.128373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.734582</td>\n",
       "      <td>0.863070</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1285.0</td>\n",
       "      <td>1333.0</td>\n",
       "      <td>0.036009</td>\n",
       "      <td>0.963991</td>\n",
       "      <td>0.037354</td>\n",
       "      <td>0.146341</td>\n",
       "      <td>0.082124</td>\n",
       "      <td>...</td>\n",
       "      <td>0.050031</td>\n",
       "      <td>0.646341</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.883109</td>\n",
       "      <td>0.199016</td>\n",
       "      <td>0.878247</td>\n",
       "      <td>0.205196</td>\n",
       "      <td>0.236767</td>\n",
       "      <td>1.753792</td>\n",
       "      <td>2.436699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.878627</td>\n",
       "      <td>0.929420</td>\n",
       "      <td>116.0</td>\n",
       "      <td>1829.0</td>\n",
       "      <td>1945.0</td>\n",
       "      <td>0.059640</td>\n",
       "      <td>0.940360</td>\n",
       "      <td>0.063423</td>\n",
       "      <td>0.353659</td>\n",
       "      <td>0.116891</td>\n",
       "      <td>...</td>\n",
       "      <td>0.059640</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.353659</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.116891</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.121753</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>2.904728</td>\n",
       "      <td>2.904728</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        min       max   bads   goods   total  bad_rate  good_rate      odds  \\\n",
       "0  0.095228  0.095228    5.0  1172.0  1177.0  0.004248   0.995752  0.004266   \n",
       "1  0.160692  0.241909   12.0  2866.0  2878.0  0.004170   0.995830  0.004187   \n",
       "2  0.304076  0.333679    4.0   926.0   930.0  0.004301   0.995699  0.004320   \n",
       "3  0.334144  0.421377   44.0  2825.0  2869.0  0.015336   0.984664  0.015575   \n",
       "4  0.476701  0.476701    1.0   484.0   485.0  0.002062   0.997938  0.002066   \n",
       "5  0.477222  0.602904   22.0  2122.0  2144.0  0.010261   0.989739  0.010368   \n",
       "6  0.603404  0.734175   76.0  2138.0  2214.0  0.034327   0.965673  0.035547   \n",
       "7  0.734582  0.863070   48.0  1285.0  1333.0  0.036009   0.963991  0.037354   \n",
       "8  0.878627  0.929420  116.0  1829.0  1945.0  0.059640   0.940360  0.063423   \n",
       "\n",
       "   bad_prop  good_prop  ...  cum_bad_rate_rev  cum_bads_prop  \\\n",
       "0  0.015244   0.074903  ...          0.020532       0.015244   \n",
       "1  0.036585   0.183166  ...          0.021827       0.051829   \n",
       "2  0.012195   0.059181  ...          0.026091       0.064024   \n",
       "3  0.134146   0.180546  ...          0.027934       0.198171   \n",
       "4  0.003049   0.030932  ...          0.032385       0.201220   \n",
       "5  0.067073   0.135617  ...          0.034311       0.268293   \n",
       "6  0.231707   0.136640  ...          0.043700       0.500000   \n",
       "7  0.146341   0.082124  ...          0.050031       0.646341   \n",
       "8  0.353659   0.116891  ...          0.059640       1.000000   \n",
       "\n",
       "   cum_bads_prop_rev  cum_goods_prop  cum_goods_prop_rev  cum_total_prop  \\\n",
       "0           1.000000        0.074903            1.000000        0.073678   \n",
       "1           0.984756        0.258069            0.925097        0.253834   \n",
       "2           0.948171        0.317249            0.741931        0.312050   \n",
       "3           0.935976        0.497795            0.682751        0.491643   \n",
       "4           0.801829        0.528728            0.502205        0.522003   \n",
       "5           0.798780        0.664345            0.471272        0.656213   \n",
       "6           0.731707        0.800984            0.335655        0.794804   \n",
       "7           0.500000        0.883109            0.199016        0.878247   \n",
       "8           0.353659        1.000000            0.116891        1.000000   \n",
       "\n",
       "   cum_total_prop_rev        ks      lift  cum_lift  \n",
       "0            1.000000  0.059659  0.206900  1.000000  \n",
       "1            0.926322  0.206239  0.203075  1.063081  \n",
       "2            0.746166  0.253225  0.209481  1.270724  \n",
       "3            0.687950  0.299624  0.746946  1.360529  \n",
       "4            0.508357  0.327508  0.100421  1.577296  \n",
       "5            0.477997  0.396052  0.499764  1.671100  \n",
       "6            0.343787  0.300984  1.671872  2.128373  \n",
       "7            0.205196  0.236767  1.753792  2.436699  \n",
       "8            0.121753 -0.000000  2.904728  2.904728  \n",
       "\n",
       "[9 rows x 22 columns]"
      ]
     },
     "execution_count": 392,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07ee064e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7cce833",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ddc58280",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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